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Achieving a sustainable future for AI

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Achieving a sustainable future for AI

Provided byIntel

We’re witnessing a historic, global paradigm shift driven by dramatic improvements in AI. As AI has evolved from predictive to generative, more businesses are taking notice, with enterprise adoption of AI greater than doubling since 2017.  In response to McKinsey, 63% of respondents expect their organizations’ investment in AI to extend over the subsequent three years.

Paralleling this unprecedented adoption of AI, the amount of compute can also be increasing at a shocking rate. Since 2012, the quantity of compute utilized in the biggest AI training runs has grown by greater than 300,000 times. Yet, as sizable computing demands grow, significant environmental implications follow.

More compute results in greater electricity consumption, and consequent carbon emissions. A 2019 study by researchers on the University of Massachusetts Amherst estimated that the electricity consumed in the course of the training of a transformer, a kind of deep learning algorithm, can emit greater than 626,000 kilos (~284 metric tons) of carbon dioxide—equal to greater than 41 round-trip flights between Recent York City and Sydney, Australia. And that’s just training the model.

We’re also facing an explosion of knowledge storage. IDC projects that 180 zettabytes of knowledge—or, 180 billion terabytes—might be created in 2025. The collective energy required for data storage at this scale is gigantic and might be difficult to handle sustainably. Depending on the conditions of knowledge storage (e.g., hardware used, energy mixture of the ability), a single terabyte of stored data can produce 2 tons of CO2 emissions annually. Now multiply that by 180 billion.

This current trajectory for intensifying AI with an ever-growing environmental footprint is solely not sustainable. We want to rethink the established order and alter our strategies and behavior.

Driving sustainable improvements with AI

While there are undoubtedly serious carbon emissions implications with the increased prominence of AI, there are also enormous opportunities. Real-time data collection combined with AI can actually businesses quickly discover areas for operational improvement to assist reduce carbon emissions at a scale.

For instance, AI models can discover immediate improvement opportunities for aspects influencing constructing efficiency, including heating, ventilation, and air-con (HVAC). As a fancy, data-rich, multi-variable system, HVAC is well-suited to automated optimization, and enhancements can result in energy savings inside just a number of months. While this chance exists in almost any constructing, it’s especially useful in data centers. Several years ago, Google shared how implementing AI to enhance data center cooling reduced its energy consumption by as much as 40%.

AI can also be proving effective for implementing carbon-aware computing. Routinely shifting computing tasks, based on the provision of renewable energy sources, can lower the carbon footprint of the activity.

Likewise, AI will help diminish the ballooning data storage problem previously mentioned. To handle the sustainability concerns of large-scale data storage, Gerry McGovern, in his book , recognized that as much as 90% of knowledge is unused—merely stored. AI will help determine what data is worthwhile, obligatory, and of high enough quality to warrant storage. Superfluous data can simply be discarded, saving each cost and energy.

Find out how to design AI projects more sustainably

To responsibly implement AI initiatives, all of us must rethink a number of things and take a more proactive approach to designing AI projects.

Begin with a critical examination of the business problem you are attempting to resolve. Ask: Do I actually need AI to resolve this problem or can traditional probabilistic methods with lower computing and energy requirements suffice? Deep learning will not be the answer to all problems, so it pays to be selective when making the determination.  

When you’ve clarified your enterprise problem or use case, fastidiously consider the next when constructing your solution and model:

  1. Emphasize data quality over data quantity. Smaller datasets require less energy for training and have lighter ongoing compute and storage implications, thereby producing fewer carbon emissions. Studies show that lots of the parameters inside a trained neural network may be pruned by as much as 99%, yielding much smaller, more sparse networks.
  2. Consider the extent of accuracy truly needed to resolve in your use case. As an example, when you were to fine-tune your models for a lower accuracy intake calculation, relatively than compute-intensive FP32 calculations, you may drive significant energy savings.
  3. Leverage domain-specific models and stop re-inventing the wheel. Orchestrating an ensemble of models from existing, trained datasets can provide you with higher outcomes. For instance, when you have already got a big model trained to grasp language semantics, you may construct a smaller, domain-specific model tailored to your needs that taps into the larger model’s knowledge base, leading to similar outputs with far more efficiency.
  4. Balance your hardware and software from edge to cloud. A more heterogenous AI infrastructure, with a mix of AI computing chipsets that meet specific application needs, will make sure you save energy across the board, from storage to networking to compute. While edge device SWaP (size, weight, and power) constraints require smaller, more efficient AI models, AI calculations closer to where data is generated may end up in more carbon-efficient computing with lower-power devices and smaller network and data storage requirements. And, for dedicated AI hardware, using built-in accelerator technologies to extend performance per watt can yield significant energy savings. Our testing shows built-in accelerators can improve average performance per watt efficiency 3.9x on targeted workloads in comparison to the identical workloads running on the identical platform without accelerators. (Results may vary.)  
  5. Consider open-source solutions with libraries of optimizations to assist make sure you’re getting the very best performance out of your hardware and frameworks out of the box. Along with open source, embracing open standards will help with repeatability and scale. For instance, to avoid energy-intensive initial model training, think about using pre-trained models for greater efficiency and the potential for shared/federated learnings and enhancements over time. Similarly, open APIs enable more efficient cross-architecture solutions, allowing you to construct tools, frameworks, and models once and deploy all over the place with more optimal performance.

Like many sustainability-led decisions, designing your AI projects to cut back their environmental impact will not be easy. Reducing your energy and carbon footprint requires work, intention, and compromise to make probably the most responsible selections. But as we see in other sustainability-led business selections, even seemingly small adjustments can create large, collective improvements to cut back carbon emissions and help slow the consequences of climate change.

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