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Energy Efficiency covers topics related to energy efficiency, savings, consumption, sufficiency, and transition in all sectors across the globe. Areas of current interest include:Evaluation and modeling of energy efficiency policies and demand-side management programs.Impact of energy efficiency economy-wide across diverse levels of governance. Contribution of energy efficiency to climate change mitigation goals -benefits or multiple benefits of energy efficiency and energy savings, especially health benefits and productivity. Policies and incentives for energy efficiency and demand-side management programs in future electricity markets with high shares of renewables and prosumers.
We are proud to acknowledge that over 50% of the articles published in this journal in 2023 were related to one or more of the 17 Sustainable Development Goals (SDGs).
The aim of this Special Issue is to collect recent information on the exposure of countries and regions to and the policy measures taken to combat the energy crisis triggered by the Russian invasion of Ukraine.
Authors are welcome to suggest suitable reviewers and/or request the exclusion of certain individuals when they submit their manuscripts.
Investments into downsized infrastructure can help enterprises reap the benefits of AI while mitigating energy consumption, says corporate VP and GM of data center platform engineering and architecture at Intel, Zane Ball.
Although AI is by no means a new technology there have been massive and rapid investments in it and large language models. However, the high-performance computing that powers these rapidly growing AI tools — and enables record automation and operational efficiency — also consumes a staggering amount of energy. With the proliferation of AI comes the responsibility to deploy that AI responsibly and with an eye to sustainability during hardware and software R&D as well as within data centers.
"Enterprises need to be very aware of the energy consumption of their digital technologies, how big it is, and how their decisions are affecting it," says corporate vice president and general manager of data center platform engineering and architecture at Intel, Zane Ball.
One of the key drivers of a more sustainable AI is modularity, says Ball. Modularity breaks down subsystems of a server into standard building blocks, defining interfaces between those blocks so they can work together. This system can reduce the amount of embodied carbon in a server''s hardware components and allows for components of the overall ecosystem to be reused, subsequently reducing R&D investments.
Downsizing infrastructure within data centers, hardware, and software can also help enterprises reach greater energy efficiency without compromising function or performance. While very large AI models require megawatts of super compute power, smaller, fine-tuned models that operate within a specific knowledge domain can maintain high performance but low energy consumption.
"You give up that kind of amazing general purpose use like when you''re using ChatGPT-4 and you can ask it everything from 17th century Italian poetry to quantum mechanics, if you narrow your range, these smaller models can give you equivalent or better kind of capability, but at a tiny fraction of the energy consumption," says Ball.
The opportunities for greater energy efficiency within AI deployment will only expand over the next three to five years. Ball forecasts significant hardware optimization strides, the rise of AI factories — facilities that train AI models on a large scale while modulating energy consumption based on its availability — as well as the continued growth of liquid cooling, driven by the need to cool the next generation of powerful AI innovations.
This episode of Business Lab is produced in partnership with Intel.
Laurel Ruma: From MIT Technology Review, I''m Laurel Ruma and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace.Our topic is building a better AI architecture. Going green isn''t for the faint of heart, but it''s also a pressing need for many, if not all enterprises. AI provides many opportunities for enterprises to make better decisions, so how can it also help them be greener?Two words for you: sustainable AI.My guest is Zane Ball, corporate vice president and general manager of data center platform engineering and architecture at Intel.This podcast is produced in partnership with Intel.Welcome Zane.
Zane Ball: Good morning.
Laurel: So to set the stage for our conversation, let''s start off with the big topic. As AI transforms businesses across industries, it brings the benefits of automation and operational efficiency, but that high-performance computing also consumes more energy. Could you give an overview of the current state of AI infrastructure and sustainability at the large enterprise level?
Now we''re looking at something I think that''s not going to pencil out. And we''re really facing a really significant growth in energy consumption in these digital services. And I think that''s concerning. And I think that means that we''ve got to take some strong actions across the industry to get on top of this. And I think just the very availability of electricity at this scale is going to be a key driver. But of course many companies have net-zero goals. And I think as we pivot into some of these AI use cases, we''ve got work to do to square all of that together.
Laurel: Yeah, as you mentioned, the challenges are trying to develop sustainable AI and making data centers more energy efficient. So could you describe what modularity is and how a modularity ecosystem can power a more sustainable AI?
Laurel: So what are some of those techniques and technologies like liquid cooling and ultrahigh dense compute that large enterprises can use to compute more efficiently? And what are their effects on water consumption, energy use, and overall performance as you were outlining earlier as well?
Laurel: Yeah, that definitely makes sense. And this is a good segue into this other part of it, which is how data centers and hardware as well software can collaborate to create greater energy efficient technology without compromising function. So how can enterprises invest in more energy efficient hardware such as hardware-aware software, and as you were mentioning earlier, large language models or LLMs with smaller downsized infrastructure but still reap the benefits of AI?
Laurel: And you''ve outlined so many of these different kind of technologies. So how can enterprise adoption of things like modularity and liquid cooling and hardware aware software be incentivized to actually make use of all these new technologies?
Zane: A year ago, I worried a lot about that question. How do we get people who are developing new applications to just be aware of the downstream implications? One of the benefits of this revolution in the last 12 months is I think just availability of electricity is going to be a big challenge for many enterprises as they seek to adopt some of these energy intensive applications. And I think the hard reality of energy availability is going to bring some very strong incentives very quickly to attack these kinds of problems.
Laurel: Well, it''s also clear there''s an imperative for enterprises that are trying to take advantage of AI to curb that energy consumption as well as meet their environmental, social, and governance or ESG goals. So what are the major challenges that come with making more sustainable AI and computing transformations?
Laurel: So could you offer an example or use case of one of those energy efficient AI driven architectures and how AI was subsequently deployed for it?
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