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    The multifaceted challenge of powering AI

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    Artificial intelligence has become vital in business and financial dealings, medical care, technology development, research, and much more. Without realizing it, consumers rely on AI when they stream a video, do online banking, or perform an online search. Behind these capabilities are more than 10,000 data centers globally, each one a huge warehouse containing thousands of computer servers and other infrastructure for storing, managing, and processing data. There are now over 5,000 data centers in the United States, and new ones are being built every day — in the U.S. and worldwide. Often dozens are clustered together right near where people live, attracted by policies that provide tax breaks and other incentives, and by what looks like abundant electricity.And data centers do consume huge amounts of electricity. U.S. data centers consumed more than 4 percent of the country’s total electricity in 2023, and by 2030 that fraction could rise to 9 percent, according to the Electric Power Research Institute. A single large data center can consume as much electricity as 50,000 homes.The sudden need for so many data centers presents a massive challenge to the technology and energy industries, government policymakers, and everyday consumers. Research scientists and faculty members at the MIT Energy Initiative (MITEI) are exploring multiple facets of this problem — from sourcing power to grid improvement to analytical tools that increase efficiency, and more. Data centers have quickly become the energy issue of our day.Unexpected demand brings unexpected solutionsSeveral companies that use data centers to provide cloud computing and data management services are announcing some surprising steps to deliver all that electricity. Proposals include building their own small nuclear plants near their data centers and even restarting one of the undamaged nuclear reactors at Three Mile Island, which has been shuttered since 2019. (A different reactor at that plant partially melted down in 1979, causing the nation’s worst nuclear power accident.) Already the need to power AI is causing delays in the planned shutdown of some coal-fired power plants and raising prices for residential consumers. Meeting the needs of data centers is not only stressing power grids, but also setting back the transition to clean energy needed to stop climate change.There are many aspects to the data center problem from a power perspective. Here are some that MIT researchers are focusing on, and why they’re important.An unprecedented surge in the demand for electricity“In the past, computing was not a significant user of electricity,” says William H. Green, director of MITEI and the Hoyt C. Hottel Professor in the MIT Department of Chemical Engineering. “Electricity was used for running industrial processes and powering household devices such as air conditioners and lights, and more recently for powering heat pumps and charging electric cars. But now all of a sudden, electricity used for computing in general, and by data centers in particular, is becoming a gigantic new demand that no one anticipated.”Why the lack of foresight? Usually, demand for electric power increases by roughly half-a-percent per year, and utilities bring in new power generators and make other investments as needed to meet the expected new demand. But the data centers now coming online are creating unprecedented leaps in demand that operators didn’t see coming. In addition, the new demand is constant. It’s critical that a data center provides its services all day, every day. There can be no interruptions in processing large datasets, accessing stored data, and running the cooling equipment needed to keep all the packed-together computers churning away without overheating.Moreover, even if enough electricity is generated, getting it to where it’s needed may be a problem, explains Deepjyoti Deka, a MITEI research scientist. “A grid is a network-wide operation, and the grid operator may have sufficient generation at another location or even elsewhere in the country, but the wires may not have sufficient capacity to carry the electricity to where it’s wanted.” So transmission capacity must be expanded — and, says Deka, that’s a slow process.Then there’s the “interconnection queue.” Sometimes, adding either a new user (a “load”) or a new generator to an existing grid can cause instabilities or other problems for everyone else already on the grid. In that situation, bringing a new data center online may be delayed. Enough delays can result in new loads or generators having to stand in line and wait for their turn. Right now, much of the interconnection queue is already filled up with new solar and wind projects. The delay is now about five years. Meeting the demand from newly installed data centers while ensuring that the quality of service elsewhere is not hampered is a problem that needs to be addressed.Finding clean electricity sourcesTo further complicate the challenge, many companies — including so-called “hyperscalers” such as Google, Microsoft, and Amazon — have made public commitments to having net-zero carbon emissions within the next 10 years. Many have been making strides toward achieving their clean-energy goals by buying “power purchase agreements.” They sign a contract to buy electricity from, say, a solar or wind facility, sometimes providing funding for the facility to be built. But that approach to accessing clean energy has its limits when faced with the extreme electricity demand of a data center.Meanwhile, soaring power consumption is delaying coal plant closures in many states. There are simply not enough sources of renewable energy to serve both the hyperscalers and the existing users, including individual consumers. As a result, conventional plants fired by fossil fuels such as coal are needed more than ever.As the hyperscalers look for sources of clean energy for their data centers, one option could be to build their own wind and solar installations. But such facilities would generate electricity only intermittently. Given the need for uninterrupted power, the data center would have to maintain energy storage units, which are expensive. They could instead rely on natural gas or diesel generators for backup power — but those devices would need to be coupled with equipment to capture the carbon emissions, plus a nearby site for permanently disposing of the captured carbon.Because of such complications, several of the hyperscalers are turning to nuclear power. As Green notes, “Nuclear energy is well matched to the demand of data centers, because nuclear plants can generate lots of power reliably, without interruption.”In a much-publicized move in September, Microsoft signed a deal to buy power for 20 years after Constellation Energy reopens one of the undamaged reactors at its now-shuttered nuclear plant at Three Mile Island, the site of the much-publicized nuclear accident in 1979. If approved by regulators, Constellation will bring that reactor online by 2028, with Microsoft buying all of the power it produces. Amazon also reached a deal to purchase power produced by another nuclear plant threatened with closure due to financial troubles. And in early December, Meta released a request for proposals to identify nuclear energy developers to help the company meet their AI needs and their sustainability goals.Other nuclear news focuses on small modular nuclear reactors (SMRs), factory-built, modular power plants that could be installed near data centers, potentially without the cost overruns and delays often experienced in building large plants. Google recently ordered a fleet of SMRs to generate the power needed by its data centers. The first one will be completed by 2030 and the remainder by 2035.Some hyperscalers are betting on new technologies. For example, Google is pursuing next-generation geothermal projects, and Microsoft has signed a contract to purchase electricity from a startup’s fusion power plant beginning in 2028 — even though the fusion technology hasn’t yet been demonstrated.Reducing electricity demandOther approaches to providing sufficient clean electricity focus on making the data center and the operations it houses more energy efficient so as to perform the same computing tasks using less power. Using faster computer chips and optimizing algorithms that use less energy are already helping to reduce the load, and also the heat generated.Another idea being tried involves shifting computing tasks to times and places where carbon-free energy is available on the grid. Deka explains: “If a task doesn’t have to be completed immediately, but rather by a certain deadline, can it be delayed or moved to a data center elsewhere in the U.S. or overseas where electricity is more abundant, cheaper, and/or cleaner? This approach is known as ‘carbon-aware computing.’” We’re not yet sure whether every task can be moved or delayed easily, says Deka. “If you think of a generative AI-based task, can it easily be separated into small tasks that can be taken to different parts of the country, solved using clean energy, and then be brought back together? What is the cost of doing this kind of division of tasks?”That approach is, of course, limited by the problem of the interconnection queue. It’s difficult to access clean energy in another region or state. But efforts are under way to ease the regulatory framework to make sure that critical interconnections can be developed more quickly and easily.What about the neighbors?A major concern running through all the options for powering data centers is the impact on residential energy consumers. When a data center comes into a neighborhood, there are not only aesthetic concerns but also more practical worries. Will the local electricity service become less reliable? Where will the new transmission lines be located? And who will pay for the new generators, upgrades to existing equipment, and so on? When new manufacturing facilities or industrial plants go into a neighborhood, the downsides are generally offset by the availability of new jobs. Not so with a data center, which may require just a couple dozen employees.There are standard rules about how maintenance and upgrade costs are shared and allocated. But the situation is totally changed by the presence of a new data center. As a result, utilities now need to rethink their traditional rate structures so as not to place an undue burden on residents to pay for the infrastructure changes needed to host data centers.MIT’s contributionsAt MIT, researchers are thinking about and exploring a range of options for tackling the problem of providing clean power to data centers. For example, they are investigating architectural designs that will use natural ventilation to facilitate cooling, equipment layouts that will permit better airflow and power distribution, and highly energy-efficient air conditioning systems based on novel materials. They are creating new analytical tools for evaluating the impact of data center deployments on the U.S. power system and for finding the most efficient ways to provide the facilities with clean energy. Other work looks at how to match the output of small nuclear reactors to the needs of a data center, and how to speed up the construction of such reactors.MIT teams also focus on determining the best sources of backup power and long-duration storage, and on developing decision support systems for locating proposed new data centers, taking into account the availability of electric power and water and also regulatory considerations, and even the potential for using what can be significant waste heat, for example, for heating nearby buildings. Technology development projects include designing faster, more efficient computer chips and more energy-efficient computing algorithms.In addition to providing leadership and funding for many research projects, MITEI is acting as a convenor, bringing together companies and stakeholders to address this issue. At MITEI’s 2024 Annual Research Conference, a panel of representatives from two hyperscalers and two companies that design and construct data centers together discussed their challenges, possible solutions, and where MIT research could be most beneficial.As data centers continue to be built, and computing continues to create an unprecedented increase in demand for electricity, Green says, scientists and engineers are in a race to provide the ideas, innovations, and technologies that can meet this need, and at the same time continue to advance the transition to a decarbonized energy system. More

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      Explained: Generative AI’s environmental impact

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      In a two-part series, MIT News explores the environmental implications of generative AI. In this article, we look at why this technology is so resource-intensive. A second piece will investigate what experts are doing to reduce genAI’s carbon footprint and other impacts.The excitement surrounding potential benefits of generative AI, from improving worker productivity to advancing scientific research, is hard to ignore. While the explosive growth of this new technology has enabled rapid deployment of powerful models in many industries, the environmental consequences of this generative AI “gold rush” remain difficult to pin down, let alone mitigate.The computational power required to train generative AI models that often have billions of parameters, such as OpenAI’s GPT-4, can demand a staggering amount of electricity, which leads to increased carbon dioxide emissions and pressures on the electric grid.Furthermore, deploying these models in real-world applications, enabling millions to use generative AI in their daily lives, and then fine-tuning the models to improve their performance draws large amounts of energy long after a model has been developed.Beyond electricity demands, a great deal of water is needed to cool the hardware used for training, deploying, and fine-tuning generative AI models, which can strain municipal water supplies and disrupt local ecosystems. The increasing number of generative AI applications has also spurred demand for high-performance computing hardware, adding indirect environmental impacts from its manufacture and transport.“When we think about the environmental impact of generative AI, it is not just the electricity you consume when you plug the computer in. There are much broader consequences that go out to a system level and persist based on actions that we take,” says Elsa A. Olivetti, professor in the Department of Materials Science and Engineering and the lead of the Decarbonization Mission of MIT’s new Climate Project.Olivetti is senior author of a 2024 paper, “The Climate and Sustainability Implications of Generative AI,” co-authored by MIT colleagues in response to an Institute-wide call for papers that explore the transformative potential of generative AI, in both positive and negative directions for society.Demanding data centersThe electricity demands of data centers are one major factor contributing to the environmental impacts of generative AI, since data centers are used to train and run the deep learning models behind popular tools like ChatGPT and DALL-E.A data center is a temperature-controlled building that houses computing infrastructure, such as servers, data storage drives, and network equipment. For instance, Amazon has more than 100 data centers worldwide, each of which has about 50,000 servers that the company uses to support cloud computing services.While data centers have been around since the 1940s (the first was built at the University of Pennsylvania in 1945 to support the first general-purpose digital computer, the ENIAC), the rise of generative AI has dramatically increased the pace of data center construction.“What is different about generative AI is the power density it requires. Fundamentally, it is just computing, but a generative AI training cluster might consume seven or eight times more energy than a typical computing workload,” says Noman Bashir, lead author of the impact paper, who is a Computing and Climate Impact Fellow at MIT Climate and Sustainability Consortium (MCSC) and a postdoc in the Computer Science and Artificial Intelligence Laboratory (CSAIL).Scientists have estimated that the power requirements of data centers in North America increased from 2,688 megawatts at the end of 2022 to 5,341 megawatts at the end of 2023, partly driven by the demands of generative AI. Globally, the electricity consumption of data centers rose to 460 terawatts in 2022. This would have made data centers the 11th largest electricity consumer in the world, between the nations of Saudi Arabia (371 terawatts) and France (463 terawatts), according to the Organization for Economic Co-operation and Development.By 2026, the electricity consumption of data centers is expected to approach 1,050 terawatts (which would bump data centers up to fifth place on the global list, between Japan and Russia).While not all data center computation involves generative AI, the technology has been a major driver of increasing energy demands.“The demand for new data centers cannot be met in a sustainable way. The pace at which companies are building new data centers means the bulk of the electricity to power them must come from fossil fuel-based power plants,” says Bashir.The power needed to train and deploy a model like OpenAI’s GPT-3 is difficult to ascertain. In a 2021 research paper, scientists from Google and the University of California at Berkeley estimated the training process alone consumed 1,287 megawatt hours of electricity (enough to power about 120 average U.S. homes for a year), generating about 552 tons of carbon dioxide.While all machine-learning models must be trained, one issue unique to generative AI is the rapid fluctuations in energy use that occur over different phases of the training process, Bashir explains.Power grid operators must have a way to absorb those fluctuations to protect the grid, and they usually employ diesel-based generators for that task.Increasing impacts from inferenceOnce a generative AI model is trained, the energy demands don’t disappear.Each time a model is used, perhaps by an individual asking ChatGPT to summarize an email, the computing hardware that performs those operations consumes energy. Researchers have estimated that a ChatGPT query consumes about five times more electricity than a simple web search.“But an everyday user doesn’t think too much about that,” says Bashir. “The ease-of-use of generative AI interfaces and the lack of information about the environmental impacts of my actions means that, as a user, I don’t have much incentive to cut back on my use of generative AI.”With traditional AI, the energy usage is split fairly evenly between data processing, model training, and inference, which is the process of using a trained model to make predictions on new data. However, Bashir expects the electricity demands of generative AI inference to eventually dominate since these models are becoming ubiquitous in so many applications, and the electricity needed for inference will increase as future versions of the models become larger and more complex.Plus, generative AI models have an especially short shelf-life, driven by rising demand for new AI applications. Companies release new models every few weeks, so the energy used to train prior versions goes to waste, Bashir adds. New models often consume more energy for training, since they usually have more parameters than their predecessors.While electricity demands of data centers may be getting the most attention in research literature, the amount of water consumed by these facilities has environmental impacts, as well.Chilled water is used to cool a data center by absorbing heat from computing equipment. It has been estimated that, for each kilowatt hour of energy a data center consumes, it would need two liters of water for cooling, says Bashir.“Just because this is called ‘cloud computing’ doesn’t mean the hardware lives in the cloud. Data centers are present in our physical world, and because of their water usage they have direct and indirect implications for biodiversity,” he says.The computing hardware inside data centers brings its own, less direct environmental impacts.While it is difficult to estimate how much power is needed to manufacture a GPU, a type of powerful processor that can handle intensive generative AI workloads, it would be more than what is needed to produce a simpler CPU because the fabrication process is more complex. A GPU’s carbon footprint is compounded by the emissions related to material and product transport.There are also environmental implications of obtaining the raw materials used to fabricate GPUs, which can involve dirty mining procedures and the use of toxic chemicals for processing.Market research firm TechInsights estimates that the three major producers (NVIDIA, AMD, and Intel) shipped 3.85 million GPUs to data centers in 2023, up from about 2.67 million in 2022. That number is expected to have increased by an even greater percentage in 2024.The industry is on an unsustainable path, but there are ways to encourage responsible development of generative AI that supports environmental objectives, Bashir says.He, Olivetti, and their MIT colleagues argue that this will require a comprehensive consideration of all the environmental and societal costs of generative AI, as well as a detailed assessment of the value in its perceived benefits.“We need a more contextual way of systematically and comprehensively understanding the implications of new developments in this space. Due to the speed at which there have been improvements, we haven’t had a chance to catch up with our abilities to measure and understand the tradeoffs,” Olivetti says. More

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      Q&A: The climate impact of generative AI

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      Vijay Gadepally, a senior staff member at MIT Lincoln Laboratory, leads a number of projects at the Lincoln Laboratory Supercomputing Center (LLSC) to make computing platforms, and the artificial intelligence systems that run on them, more efficient. Here, Gadepally discusses the increasing use of generative AI in everyday tools, its hidden environmental impact, and some of the ways that Lincoln Laboratory and the greater AI community can reduce emissions for a greener future.Q: What trends are you seeing in terms of how generative AI is being used in computing?A: Generative AI uses machine learning (ML) to create new content, like images and text, based on data that is inputted into the ML system. At the LLSC we design and build some of the largest academic computing platforms in the world, and over the past few years we’ve seen an explosion in the number of projects that need access to high-performance computing for generative AI. We’re also seeing how generative AI is changing all sorts of fields and domains — for example, ChatGPT is already influencing the classroom and the workplace faster than regulations can seem to keep up.We can imagine all sorts of uses for generative AI within the next decade or so, like powering highly capable virtual assistants, developing new drugs and materials, and even improving our understanding of basic science. We can’t predict everything that generative AI will be used for, but I can certainly say that with more and more complex algorithms, their compute, energy, and climate impact will continue to grow very quickly.Q: What strategies is the LLSC using to mitigate this climate impact?A: We’re always looking for ways to make computing more efficient, as doing so helps our data center make the most of its resources and allows our scientific colleagues to push their fields forward in as efficient a manner as possible.As one example, we’ve been reducing the amount of power our hardware consumes by making simple changes, similar to dimming or turning off lights when you leave a room. In one experiment, we reduced the energy consumption of a group of graphics processing units by 20 percent to 30 percent, with minimal impact on their performance, by enforcing a power cap. This technique also lowered the hardware operating temperatures, making the GPUs easier to cool and longer lasting.Another strategy is changing our behavior to be more climate-aware. At home, some of us might choose to use renewable energy sources or intelligent scheduling. We are using similar techniques at the LLSC — such as training AI models when temperatures are cooler, or when local grid energy demand is low.We also realized that a lot of the energy spent on computing is often wasted, like how a water leak increases your bill but without any benefits to your home. We developed some new techniques that allow us to monitor computing workloads as they are running and then terminate those that are unlikely to yield good results. Surprisingly, in a number of cases we found that the majority of computations could be terminated early without compromising the end result.Q: What’s an example of a project you’ve done that reduces the energy output of a generative AI program?A: We recently built a climate-aware computer vision tool. Computer vision is a domain that’s focused on applying AI to images; so, differentiating between cats and dogs in an image, correctly labeling objects within an image, or looking for components of interest within an image.In our tool, we included real-time carbon telemetry, which produces information about how much carbon is being emitted by our local grid as a model is running. Depending on this information, our system will automatically switch to a more energy-efficient version of the model, which typically has fewer parameters, in times of high carbon intensity, or a much higher-fidelity version of the model in times of low carbon intensity.By doing this, we saw a nearly 80 percent reduction in carbon emissions over a one- to two-day period. We recently extended this idea to other generative AI tasks such as text summarization and found the same results. Interestingly, the performance sometimes improved after using our technique!Q: What can we do as consumers of generative AI to help mitigate its climate impact?A: As consumers, we can ask our AI providers to offer greater transparency. For example, on Google Flights, I can see a variety of options that indicate a specific flight’s carbon footprint. We should be getting similar kinds of measurements from generative AI tools so that we can make a conscious decision on which product or platform to use based on our priorities.We can also make an effort to be more educated on generative AI emissions in general. Many of us are familiar with vehicle emissions, and it can help to talk about generative AI emissions in comparative terms. People may be surprised to know, for example, that one image-generation task is roughly equivalent to driving four miles in a gas car, or that it takes the same amount of energy to charge an electric car as it does to generate about 1,500 text summarizations.There are many cases where customers would be happy to make a trade-off if they knew the trade-off’s impact.Q: What do you see for the future?A: Mitigating the climate impact of generative AI is one of those problems that people all over the world are working on, and with a similar goal. We’re doing a lot of work here at Lincoln Laboratory, but its only scratching at the surface. In the long term, data centers, AI developers, and energy grids will need to work together to provide “energy audits” to uncover other unique ways that we can improve computing efficiencies. We need more partnerships and more collaboration in order to forge ahead.If you’re interested in learning more, or collaborating with Lincoln Laboratory on these efforts, please contact Vijay Gadepally.

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      A nonflammable battery to power a safer, decarbonized future

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      Lithium-ion batteries are the workhorses of home electronics and are powering an electric revolution in transportation. But they are not suitable for every application.A key drawback is their flammability and toxicity, which make large-scale lithium-ion energy storage a bad fit in densely populated city centers and near metal processing or chemical manufacturing plants.Now Alsym Energy has developed a nonflammable, nontoxic alternative to lithium-ion batteries to help renewables like wind and solar bridge the gap in a broader range of sectors. The company’s electrodes use relatively stable, abundant materials, and its electrolyte is primarily water with some nontoxic add-ons.“Renewables are intermittent, so you need storage, and to really solve the decarbonization problem, we need to be able to make these batteries anywhere at low cost,” says Alsym co-founder and MIT Professor Kripa Varanasi.The company believes its batteries, which are currently being tested by potential customers around the world, hold enormous potential to decarbonize the high-emissions industrial manufacturing sector, and they see other applications ranging from mining to powering data centers, homes, and utilities.“We are enabling a decarbonization of markets that was not possible before,” Alsym co-founder and CEO Mukesh Chatter says. “No chemical or steel plant would dare put a lithium battery close to their premises because of the flammability, and industrial emissions are a much bigger problem than passenger cars. With this approach, we’re able to offer a new path.”Helping 1 billion peopleChatter started a telecommunications company with serial entrepreneurs and longtime members of the MIT community Ray Stata ’57, SM ’58 and Alec Dingee ’52 in 1997. Since the company was acquired in 1999, Chatter and his wife have started other ventures and invested in some startups, but after losing his mother to cancer in 2012, Chatter decided he wanted to maximize his impact by only working on technologies that could reach 1 billion people or more.The problem Chatter decided to focus on was electricity access.“The intent was to light up the homes of at least 1 billion people around the world who either did not have electricity, or only got it part of the time, condemning them basically to a life of poverty in the 19th century,” Chatter says. “When you don’t have access to electricity, you also don’t have the internet, cell phones, education, etc.”To solve the problem, Chatter decided to fund research into a new kind of battery. The battery had to be cheap enough to be adopted in low-resource settings, safe enough to be deployed in crowded areas, and work well enough to support two light bulbs, a fan, a refrigerator, and an internet modem.At first, Chatter was surprised how few takers he had to start the research, even from researchers at the top universities in the world.“It’s a burning problem, but the risk of failure was so high that nobody wanted to take the chance,” Chatter recalls.He finally found his partners in Varanasi, Rensselaer Polytechnic Institute Professor Nikhil Koratkar and Rensselaer researcher Rahul Mukherjee. Varanasi, who notes he’s been at MIT for 22 years, says the Institute’s culture gave him the confidence to tackle big problems.“My students, postdocs, and colleagues are inspirational to me,” he says. “The MIT ecosystem infuses us with this resolve to go after problems that look insurmountable.”Varanasi leads an interdisciplinary lab at MIT dedicated to understanding physicochemical and biological phenomena. His research has spurred the creation of materials, devices, products, and processes to tackle challenges in energy, agriculture, and other sectors, as well as startup companies to commercialize this work.“Working at the interfaces of matter has unlocked numerous new research pathways across various fields, and MIT has provided me the creative freedom to explore, discover, and learn, and apply that knowledge to solve critical challenges,” he says. “I was able to draw significantly from my learnings as we set out to develop the new battery technology.”Alsym’s founding team began by trying to design a battery from scratch based on new materials that could fit the parameters defined by Chatter. To make it nonflammable and nontoxic, the founders wanted to avoid lithium and cobalt.After evaluating many different chemistries, the founders settled on Alsym’s current approach, which was finalized in 2020.Although the full makeup of Alsym’s battery is still under wraps as the company waits to be granted patents, one of Alsym’s electrodes is made mostly of manganese oxide while the other is primarily made of a metal oxide. The electrolyte is primarily water.There are several advantages to Alsym’s new battery chemistry. Because the battery is inherently safer and more sustainable than lithium-ion, the company doesn’t need the same safety protections or cooling equipment, and it can pack its batteries close to each other without fear of fires or explosions. Varanasi also says the battery can be manufactured in any of today’s lithium-ion plants with minimal changes and at significantly lower operating cost.“We are very excited right now,” Chatter says. “We started out wanting to light up 1 billion people’s homes, and now in addition to the original goal we have a chance to impact the entire globe if we are successful at cutting back industrial emissions.”A new platform for energy storageAlthough the batteries don’t quite reach the energy density of lithium-ion batteries, Varanasi says Alsym is first among alternative chemistries at the system-level. He says 20-foot containers of Alsym’s batteries can provide 1.7 megawatt hours of electricity. The batteries can also fast-charge over four hours and can be configured to discharge over anywhere from two to 110 hours.“We’re highly configurable, and that’s important because depending on where you are, you can sometimes run on two cycles a day with solar, and in combination with wind, you could truly get 24/7 electricity,” Chatter says. “The need to do multiday or long duration storage is a small part of the market, but we support that too.”Alsym has been manufacturing prototypes at a small facility in Woburn, Massachusetts, for the last two years, and early this year it expanded its capacity and began to send samples to customers for field testing.In addition to large utilities, the company is working with municipalities, generator manufacturers, and providers of behind-the-meter power for residential and commercial buildings. The company is also in discussion with a large chemical manufacturers and metal processing plants to provide energy storage system to reduce their carbon footprint, something they say was not feasible with lithium-ion batteries, due to their flammability, or with nonlithium batteries, due to their large space requirements.Another critical area is data centers. With the growth of AI, the demand for data centers — and their energy consumption — is set to surge.“We must power the AI and digitization revolution without compromising our planet,” says Varanasi, adding that lithium batteries are unsuitable for co-location with data centers due to flammability risks. “Alsym batteries are well-positioned to offer a safer, more sustainable alternative. Intermittency is also a key issue for electrolyzers used in green hydrogen production and other markets.”Varanasi sees Alsym as a platform company, and Chatter says Alsym is already working on other battery chemistries that have higher densities and maintain performance at even more extreme temperatures.“When you use a single material in any battery, and the whole world starts to use it, you run out of that material,” Varanasi says. “What we have is a platform that has enabled us to not just to come up with just one chemistry, but at least three or four chemistries targeted at different applications so no one particular set of materials will be stressed in terms of supply.” More

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      Two MIT teams selected for NSF sustainable materials grants

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      Two teams led by MIT researchers were selected in December 2023 by the U.S. National Science Foundation (NSF) Convergence Accelerator, a part of the TIP Directorate, to receive awards of $5 million each over three years, to pursue research aimed at helping to bring cutting-edge new sustainable materials and processes from the lab into practical, full-scale industrial production. The selection was made after 16 teams from around the country were chosen last year for one-year grants to develop detailed plans for further research aimed at solving problems of sustainability and scalability for advanced electronic products.

      Of the two MIT-led teams chosen for this current round of funding, one team, Topological Electric, is led by Mingda Li, an associate professor in the Department of Nuclear Science and Engineering. This team will be finding pathways to scale up sustainable topological materials, which have the potential to revolutionize next-generation microelectronics by showing superior electronic performance, such as dissipationless states or high-frequency response. The other team, led by Anuradha Agarwal, a principal research scientist at MIT’s Materials Research Laboratory, will be focusing on developing new materials, devices, and manufacturing processes for microchips that minimize energy consumption using electronic-photonic integration, and that detect and avoid the toxic or scarce materials used in today’s production methods.

      Scaling the use of topological materials

      Li explains that some materials based on quantum effects have achieved successful transitions from lab curiosities to successful mass production, such as blue-light LEDs, and giant magnetorestance (GMR) devices used for magnetic data storage. But he says there are a variety of equally promising materials that have shown promise but have yet to make it into real-world applications.

      “What we really wanted to achieve is to bring newer-generation quantum materials into technology and mass production, for the benefit of broader society,” he says. In particular, he says, “topological materials are really promising to do many different things.”

      Topological materials are ones whose electronic properties are fundamentally protected against disturbance. For example, Li points to the fact that just in the last two years, it has been shown that some topological materials are even better electrical conductors than copper, which are typically used for the wires interconnecting electronic components. But unlike the blue-light LEDs or the GMR devices, which have been widely produced and deployed, when it comes to topological materials, “there’s no company, no startup, there’s really no business out there,” adds Tomas Palacios, the Clarence J. Lebel Professor in Electrical Engineering at MIT and co-principal investigator on Li’s team. Part of the reason is that many versions of such materials are studied “with a focus on fundamental exotic physical properties with little or no consideration on the sustainability aspects,” says Liang Fu, an MIT professor of physics and also a co-PI. Their team will be looking for alternative formulations that are more amenable to mass production.

      One possible application of these topological materials is for detecting terahertz radiation, explains Keith Nelson, an MIT professor of chemistry and co-PI. This extremely high-frequency electronics can carry far more information than conventional radio or microwaves, but at present there are no mature electronic devices available that are scalable at this frequency range. “There’s a whole range of possibilities for topological materials” that could work at these frequencies, he says. In addition, he says, “we hope to demonstrate an entire prototype system like this in a single, very compact solid-state platform.”

      Li says that among the many possible applications of topological devices for microelectronics devices of various kinds, “we don’t know which, exactly, will end up as a product, or will reach real industrial scaleup. That’s why this opportunity from NSF is like a bridge, which is precious, to allow us to dig deeper to unleash the true potential.”

      In addition to Li, Palacios, Fu, and Nelson, the Topological Electric team includes Qiong Ma, assistant professor of physics in Boston College; Farnaz Niroui, assistant professor of electrical engineering and computer science at MIT; Susanne Stemmer, professor of materials at the University of California at Santa Barbara; Judy Cha, professor of materials science and engineering at Cornell University; industrial partners including IBM, Analog Devices, and Raytheon; and professional consultants. “We are taking this opportunity seriously,” Li says. “We really want to see if the topological materials are as good as we show in the lab when being scaled up, and how far we can push to broadly industrialize them.”

      Toward sustainable microchip production and use

      The microchips behind everything from smartphones to medical imaging are associated with a significant percentage of greenhouse gas emissions today, and every year the world produces more than 50 million metric tons of electronic waste, the equivalent of about 5,000 Eiffel Towers. Further, the data centers necessary for complex computations and huge amount of data transfer — think AI and on-demand video — are growing and will require 10 percent of the world’s electricity by 2030.

      “The current microchip manufacturing supply chain, which includes production, distribution, and use, is neither scalable nor sustainable, and cannot continue. We must innovate our way out of this crisis,” says Agarwal.

      The name of Agarwal’s team, FUTUR-IC, is a reference to the future of the integrated circuits, or chips, through a global alliance for sustainable microchip manufacturing. Says Agarwal, “We bring together stakeholders from industry, academia, and government to co-optimize across three dimensions: technology, ecology, and workforce. These were identified as key interrelated areas by some 140 stakeholders. With FUTUR-IC we aim to cut waste and CO2-equivalent emissions associated with electronics by 50 percent every 10 years.”

      The market for microelectronics in the next decade is predicted to be on the order of a trillion dollars, but most of the manufacturing for the industry occurs only in limited geographical pockets around the world. FUTUR-IC aims to diversify and strengthen the supply chain for manufacturing and packaging of electronics. The alliance has 26 collaborators and is growing. Current external collaborators include the International Electronics Manufacturing Initiative (iNEMI), Tyndall National Institute, SEMI, Hewlett Packard Enterprise, Intel, and the Rochester Institute of Technology.

      Agarwal leads FUTUR-IC in close collaboration with others, including, from MIT, Lionel Kimerling, the Thomas Lord Professor of Materials Science and Engineering; Elsa Olivetti, the Jerry McAfee Professor in Engineering; Randolph Kirchain, principal research scientist in the Materials Research Laboratory; and Greg Norris, director of MIT’s Sustainability and Health Initiative for NetPositive Enterprise (SHINE). All are affiliated with the Materials Research Laboratory. They are joined by Samuel Serna, an MIT visiting professor and assistant professor of physics at Bridgewater State University. Other key personnel include Sajan Saini, education director for the Initiative for Knowledge and Innovation in Manufacturing in MIT’s Department of Materials Science and Engineering; Peter O’Brien, a professor from Tyndall National Institute; and Shekhar Chandrashekhar, CEO of iNEMI.

      “We expect the integration of electronics and photonics to revolutionize microchip manufacturing, enhancing efficiency, reducing energy consumption, and paving the way for unprecedented advancements in computing speed and data-processing capabilities,” says Serna, who is the co-lead on the project’s technology “vector.”

      Common metrics for these efforts are needed, says Norris, co-lead for the ecology vector, adding, “The microchip industry must have transparent and open Life Cycle Assessment (LCA) models and data, which are being developed by FUTUR-IC.” This is especially important given that microelectronics production transcends industries. “Given the scale and scope of microelectronics, it is critical for the industry to lead in the transition to sustainable manufacture and use,” says Kirchain, another co-lead and the co-director of the Concrete Sustainability Hub at MIT. To bring about this cross-fertilization, co-lead Olivetti, also co-director of the MIT Climate and Sustainability Consortium (MCSC), will collaborate with FUTUR-IC to enhance the benefits from microchip recycling, leveraging the learning across industries.

      Saini, the co-lead for the workforce vector, stresses the need for agility. “With a workforce that adapts to a practice of continuous upskilling, we can help increase the robustness of the chip-manufacturing supply chain, and validate a new design for a sustainability curriculum,” he says.

      “We have become accustomed to the benefits forged by the exponential growth of microelectronic technology performance and market size,” says Kimerling, who is also director of MIT’s Materials Research Laboratory and co-director of the MIT Microphotonics Center. “The ecological impact of this growth in terms of materials use, energy consumption and end-of-life disposal has begun to push back against this progress. We believe that concurrently engineered solutions for these three dimensions will build a common learning curve to power the next 40 years of progress in the semiconductor industry.”

      The MIT teams are two of six that received awards addressing sustainable materials for global challenges through phase two of the NSF Convergence Accelerator program. Launched in 2019, the program targets solutions to especially compelling challenges at an accelerated pace by incorporating a multidisciplinary research approach. More

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      in Energy

      Propelling atomically layered magnets toward green computers

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      Globally, computation is booming at an unprecedented rate, fueled by the boons of artificial intelligence. With this, the staggering energy demand of the world’s computing infrastructure has become a major concern, and the development of computing devices that are far more energy-efficient is a leading challenge for the scientific community. 

      Use of magnetic materials to build computing devices like memories and processors has emerged as a promising avenue for creating “beyond-CMOS” computers, which would use far less energy compared to traditional computers. Magnetization switching in magnets can be used in computation the same way that a transistor switches from open or closed to represent the 0s and 1s of binary code. 

      While much of the research along this direction has focused on using bulk magnetic materials, a new class of magnetic materials — called two-dimensional van der Waals magnets — provides superior properties that can improve the scalability and energy efficiency of magnetic devices to make them commercially viable. 

      Although the benefits of shifting to 2D magnetic materials are evident, their practical induction into computers has been hindered by some fundamental challenges. Until recently, 2D magnetic materials could operate only at very low temperatures, much like superconductors. So bringing their operating temperatures above room temperature has remained a primary goal. Additionally, for use in computers, it is important that they can be controlled electrically, without the need for magnetic fields. Bridging this fundamental gap, where 2D magnetic materials can be electrically switched above room temperature without any magnetic fields, could potentially catapult the translation of 2D magnets into the next generation of “green” computers.

      A team of MIT researchers has now achieved this critical milestone by designing a “van der Waals atomically layered heterostructure” device where a 2D van der Waals magnet, iron gallium telluride, is interfaced with another 2D material, tungsten ditelluride. In an open-access paper published March 15 in Science Advances, the team shows that the magnet can be toggled between the 0 and 1 states simply by applying pulses of electrical current across their two-layer device. 

      Play video

      The Future of Spintronics: Manipulating Spins in Atomic Layers without External Magnetic FieldsVideo: Deblina Sarkar

      “Our device enables robust magnetization switching without the need for an external magnetic field, opening up unprecedented opportunities for ultra-low power and environmentally sustainable computing technology for big data and AI,” says lead author Deblina Sarkar, the AT&T Career Development Assistant Professor at the MIT Media Lab and Center for Neurobiological Engineering, and head of the Nano-Cybernetic Biotrek research group. “Moreover, the atomically layered structure of our device provides unique capabilities including improved interface and possibilities of gate voltage tunability, as well as flexible and transparent spintronic technologies.”

      Sarkar is joined on the paper by first author Shivam Kajale, a graduate student in Sarkar’s research group at the Media Lab; Thanh Nguyen, a graduate student in the Department of Nuclear Science and Engineering (NSE); Nguyen Tuan Hung, an MIT visiting scholar in NSE and an assistant professor at Tohoku University in Japan; and Mingda Li, associate professor of NSE.

      Breaking the mirror symmetries 

      When electric current flows through heavy metals like platinum or tantalum, the electrons get segregated in the materials based on their spin component, a phenomenon called the spin Hall effect, says Kajale. The way this segregation happens depends on the material, and particularly its symmetries.

      “The conversion of electric current to spin currents in heavy metals lies at the heart of controlling magnets electrically,” Kajale notes. “The microscopic structure of conventionally used materials, like platinum, have a kind of mirror symmetry, which restricts the spin currents only to in-plane spin polarization.”

      Kajale explains that two mirror symmetries must be broken to produce an “out-of-plane” spin component that can be transferred to a magnetic layer to induce field-free switching. “Electrical current can ‘break’ the mirror symmetry along one plane in platinum, but its crystal structure prevents the mirror symmetry from being broken in a second plane.”

      In their earlier experiments, the researchers used a small magnetic field to break the second mirror plane. To get rid of the need for a magnetic nudge, Kajale and Sarkar and colleagues looked instead for a material with a structure that could break the second mirror plane without outside help. This led them to another 2D material, tungsten ditelluride. The tungsten ditelluride that the researchers used has an orthorhombic crystal structure. The material itself has one broken mirror plane. Thus, by applying current along its low-symmetry axis (parallel to the broken mirror plane), the resulting spin current has an out-of-plane spin component that can directly induce switching in the ultra-thin magnet interfaced with the tungsten ditelluride. 

      “Because it’s also a 2D van der Waals material, it can also ensure that when we stack the two materials together, we get pristine interfaces and a good flow of electron spins between the materials,” says Kajale. 

      Becoming more energy-efficient 

      Computer memory and processors built from magnetic materials use less energy than traditional silicon-based devices. And the van der Waals magnets can offer higher energy efficiency and better scalability compared to bulk magnetic material, the researchers note. 

      The electrical current density used for switching the magnet translates to how much energy is dissipated during switching. A lower density means a much more energy-efficient material. “The new design has one of the lowest current densities in van der Waals magnetic materials,” Kajale says. “This new design has an order of magnitude lower in terms of the switching current required in bulk materials. This translates to something like two orders of magnitude improvement in energy efficiency.”

      The research team is now looking at similar low-symmetry van der Waals materials to see if they can reduce current density even further. They are also hoping to collaborate with other researchers to find ways to manufacture the 2D magnetic switch devices at commercial scale. 

      This work was carried out, in part, using the facilities at MIT.nano. It was funded by the Media Lab, the U.S. National Science Foundation, and the U.S. Department of Energy. More

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      in Environment

      MIT-led teams win National Science Foundation grants to research sustainable materials

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      Three MIT-led teams are among 16 nationwide to receive funding awards to address sustainable materials for global challenges through the National Science Foundation’s Convergence Accelerator program. Launched in 2019, the program targets solutions to especially compelling societal or scientific challenges at an accelerated pace, by incorporating a multidisciplinary research approach.

      “Solutions for today’s national-scale societal challenges are hard to solve within a single discipline. Instead, these challenges require convergence to merge ideas, approaches, and technologies from a wide range of diverse sectors, disciplines, and experts,” the NSF explains in its description of the Convergence Accelerator program. Phase 1 of the award involves planning to expand initial concepts, identify new team members, participate in an NSF development curriculum, and create an early prototype.

      Sustainable microchips

      One of the funded projects, “Building a Sustainable, Innovative Ecosystem for Microchip Manufacturing,” will be led by Anuradha Murthy Agarwal, a principal research scientist at the MIT Materials Research Laboratory. The aim of this project is to help transition the manufacturing of microchips to more sustainable processes that, for example, can reduce e-waste landfills by allowing repair of chips, or enable users to swap out a rogue chip in a motherboard rather than tossing out the entire laptop or cellphone.

      “Our goal is to help transition microchip manufacturing towards a sustainable industry,” says Agarwal. “We aim to do that by partnering with industry in a multimodal approach that prototypes technology designs to minimize energy consumption and waste generation, retrains the semiconductor workforce, and creates a roadmap for a new industrial ecology to mitigate materials-critical limitations and supply-chain constraints.”

      Agarwal’s co-principal investigators are Samuel Serna, an MIT visiting professor and assistant professor of physics at Bridgewater State University, and two MIT faculty affiliated with the Materials Research Laboratory: Juejun Hu, the John Elliott Professor of Materials Science and Engineering; and Lionel Kimerling, the Thomas Lord Professor of Materials Science and Engineering.

      The training component of the project will also create curricula for multiple audiences. “At Bridgewater State University, we will create a new undergraduate course on microchip manufacturing sustainability, and eventually adapt it for audiences from K-12, as well as incumbent employees,” says Serna.

      Sajan Saini and Erik Verlage of the MIT Department of Materials Science and Engineering (DMSE), and Randolph Kirchain from the MIT Materials Systems Laboratory, who have led MIT initiatives in virtual reality digital education, materials criticality, and roadmapping, are key contributors. The project also includes DMSE graduate students Drew Weninger and Luigi Ranno, and undergraduate Samuel Bechtold from Bridgewater State University’s Department of Physics.

      Sustainable topological materials

      Under the direction of Mingda Li, the Class of 1947 Career Development Professor and an Associate Professor of Nuclear Science and Engineering, the “Sustainable Topological Energy Materials (STEM) for Energy-efficient Applications” project will accelerate research in sustainable topological quantum materials.

      Topological materials are ones that retain a particular property through all external disturbances. Such materials could potentially be a boon for quantum computing, which has so far been plagued by instability, and would usher in a post-silicon era for microelectronics. Even better, says Li, topological materials can do their job without dissipating energy even at room temperatures.

      Topological materials can find a variety of applications in quantum computing, energy harvesting, and microelectronics. Despite their promise, and a few thousands of potential candidates, discovery and mass production of these materials has been challenging. Topology itself is not a measurable characteristic so researchers have to first develop ways to find hints of it. Synthesis of materials and related process optimization can take months, if not years, Li adds. Machine learning can accelerate the discovery and vetting stage.

      Given that a best-in-class topological quantum material has the potential to disrupt the semiconductor and computing industries, Li and team are paying special attention to the environmental sustainability of prospective materials. For example, some potential candidates include gold, lead, or cadmium, whose scarcity or toxicity does not lend itself to mass production and have been disqualified.

      Co-principal investigators on the project include Liang Fu, associate professor of physics at MIT; Tomas Palacios, professor of electrical engineering and computer science at MIT and director of the Microsystems Technology Laboratories; Susanne Stemmer of the University of California at Santa Barbara; and Qiong Ma of Boston College. The $750,000 one-year Phase 1 grant will focus on three priorities: building a topological materials database; identifying the most environmentally sustainable candidates for energy-efficient topological applications; and building the foundation for a Center for Sustainable Topological Energy Materials at MIT that will encourage industry-academia collaborations.

      At a time when the size of silicon-based electronic circuit boards is reaching its lower limit, the promise of topological materials whose conductivity increases with decreasing size is especially attractive, Li says. In addition, topological materials can harvest wasted heat: Imagine using your body heat to power your phone. “There are different types of application scenarios, and we can go much beyond the capabilities of existing materials,” Li says, “the possibilities of topological materials are endlessly exciting.”

      Socioresilient materials design

      Researchers in the MIT Department of Materials Science and Engineering (DMSE) have been awarded $750,000 in a cross-disciplinary project that aims to fundamentally redirect materials research and development toward more environmentally, socially, and economically sustainable and resilient materials. This “socioresilient materials design” will serve as the foundation for a new research and development framework that takes into account technical, environmental, and social factors from the beginning of the materials design and development process.

      Christine Ortiz, the Morris Cohen Professor of Materials Science and Engineering, and Ellan Spero PhD ’14, an instructor in DMSE, are leading this research effort, which includes Cornell University, the University of Swansea, Citrine Informatics, Station1, and 14 other organizations in academia, industry, venture capital, the social sector, government, and philanthropy.

      The team’s project, “Mind Over Matter: Socioresilient Materials Design,” emphasizes that circular design approaches, which aim to minimize waste and maximize the reuse, repair, and recycling of materials, are often insufficient to address negative repercussions for the planet and for human health and safety.

      Too often society understands the unintended negative consequences long after the materials that make up our homes and cities and systems have been in production and use for many years. Examples include disparate and negative public health impacts due to industrial scale manufacturing of materials, water and air contamination with harmful materials, and increased risk of fire in lower-income housing buildings due to flawed materials usage and design. Adverse climate events including drought, flood, extreme temperatures, and hurricanes have accelerated materials degradation, for example in critical infrastructure, leading to amplified environmental damage and social injustice. While classical materials design and selection approaches are insufficient to address these challenges, the new research project aims to do just that.

      “The imagination and technical expertise that goes into materials design is too often separated from the environmental and social realities of extraction, manufacturing, and end-of-life for materials,” says Ortiz. 

      Drawing on materials science and engineering, chemistry, and computer science, the project will develop a framework for materials design and development. It will incorporate powerful computational capabilities — artificial intelligence and machine learning with physics-based materials models — plus rigorous methodologies from the social sciences and the humanities to understand what impacts any new material put into production could have on society. More

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      in Energy

      Computers that power self-driving cars could be a huge driver of global carbon emissions

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      In the future, the energy needed to run the powerful computers on board a global fleet of autonomous vehicles could generate as many greenhouse gas emissions as all the data centers in the world today.

      That is one key finding of a new study from MIT researchers that explored the potential energy consumption and related carbon emissions if autonomous vehicles are widely adopted.

      The data centers that house the physical computing infrastructure used for running applications are widely known for their large carbon footprint: They currently account for about 0.3 percent of global greenhouse gas emissions, or about as much carbon as the country of Argentina produces annually, according to the International Energy Agency. Realizing that less attention has been paid to the potential footprint of autonomous vehicles, the MIT researchers built a statistical model to study the problem. They determined that 1 billion autonomous vehicles, each driving for one hour per day with a computer consuming 840 watts, would consume enough energy to generate about the same amount of emissions as data centers currently do.

      The researchers also found that in over 90 percent of modeled scenarios, to keep autonomous vehicle emissions from zooming past current data center emissions, each vehicle must use less than 1.2 kilowatts of power for computing, which would require more efficient hardware. In one scenario — where 95 percent of the global fleet of vehicles is autonomous in 2050, computational workloads double every three years, and the world continues to decarbonize at the current rate — they found that hardware efficiency would need to double faster than every 1.1 years to keep emissions under those levels.

      “If we just keep the business-as-usual trends in decarbonization and the current rate of hardware efficiency improvements, it doesn’t seem like it is going to be enough to constrain the emissions from computing onboard autonomous vehicles. This has the potential to become an enormous problem. But if we get ahead of it, we could design more efficient autonomous vehicles that have a smaller carbon footprint from the start,” says first author Soumya Sudhakar, a graduate student in aeronautics and astronautics.

      Sudhakar wrote the paper with her co-advisors Vivienne Sze, associate professor in the Department of Electrical Engineering and Computer Science (EECS) and a member of the Research Laboratory of Electronics (RLE); and Sertac Karaman, associate professor of aeronautics and astronautics and director of the Laboratory for Information and Decision Systems (LIDS). The research appears today in the January-February issue of IEEE Micro.

      Modeling emissions

      The researchers built a framework to explore the operational emissions from computers on board a global fleet of electric vehicles that are fully autonomous, meaning they don’t require a back-up human driver.

      The model is a function of the number of vehicles in the global fleet, the power of each computer on each vehicle, the hours driven by each vehicle, and the carbon intensity of the electricity powering each computer.

      “On its own, that looks like a deceptively simple equation. But each of those variables contains a lot of uncertainty because we are considering an emerging application that is not here yet,” Sudhakar says.

      For instance, some research suggests that the amount of time driven in autonomous vehicles might increase because people can multitask while driving and the young and the elderly could drive more. But other research suggests that time spent driving might decrease because algorithms could find optimal routes that get people to their destinations faster.

      In addition to considering these uncertainties, the researchers also needed to model advanced computing hardware and software that doesn’t exist yet.

      To accomplish that, they modeled the workload of a popular algorithm for autonomous vehicles, known as a multitask deep neural network because it can perform many tasks at once. They explored how much energy this deep neural network would consume if it were processing many high-resolution inputs from many cameras with high frame rates, simultaneously.

      When they used the probabilistic model to explore different scenarios, Sudhakar was surprised by how quickly the algorithms’ workload added up.

      For example, if an autonomous vehicle has 10 deep neural networks processing images from 10 cameras, and that vehicle drives for one hour a day, it will make 21.6 million inferences each day. One billion vehicles would make 21.6 quadrillion inferences. To put that into perspective, all of Facebook’s data centers worldwide make a few trillion inferences each day (1 quadrillion is 1,000 trillion).

      “After seeing the results, this makes a lot of sense, but it is not something that is on a lot of people’s radar. These vehicles could actually be using a ton of computer power. They have a 360-degree view of the world, so while we have two eyes, they may have 20 eyes, looking all over the place and trying to understand all the things that are happening at the same time,” Karaman says.

      Autonomous vehicles would be used for moving goods, as well as people, so there could be a massive amount of computing power distributed along global supply chains, he says. And their model only considers computing — it doesn’t take into account the energy consumed by vehicle sensors or the emissions generated during manufacturing.

      Keeping emissions in check

      To keep emissions from spiraling out of control, the researchers found that each autonomous vehicle needs to consume less than 1.2 kilowatts of energy for computing. For that to be possible, computing hardware must become more efficient at a significantly faster pace, doubling in efficiency about every 1.1 years.

      One way to boost that efficiency could be to use more specialized hardware, which is designed to run specific driving algorithms. Because researchers know the navigation and perception tasks required for autonomous driving, it could be easier to design specialized hardware for those tasks, Sudhakar says. But vehicles tend to have 10- or 20-year lifespans, so one challenge in developing specialized hardware would be to “future-proof” it so it can run new algorithms.

      In the future, researchers could also make the algorithms more efficient, so they would need less computing power. However, this is also challenging because trading off some accuracy for more efficiency could hamper vehicle safety.

      Now that they have demonstrated this framework, the researchers want to continue exploring hardware efficiency and algorithm improvements. In addition, they say their model can be enhanced by characterizing embodied carbon from autonomous vehicles — the carbon emissions generated when a car is manufactured — and emissions from a vehicle’s sensors.

      While there are still many scenarios to explore, the researchers hope that this work sheds light on a potential problem people may not have considered.

      “We are hoping that people will think of emissions and carbon efficiency as important metrics to consider in their designs. The energy consumption of an autonomous vehicle is really critical, not just for extending the battery life, but also for sustainability,” says Sze.

      This research was funded, in part, by the National Science Foundation and the MIT-Accenture Fellowship. More