An intellectual overlap of pure mathematics and engineering techniques targeted to develop self-reliant, efficient, and clean artificial intelligence processors

耗能处理器广泛应用于快速发展的强大云计算和人工智能。由于对深度学习的推崇，人工智能的功耗大大增加，深度学习使用非常大的数学模型处理大量数据，这些模型可以由数亿甚至数十亿个参数组成。它消耗大量的能源，产生大量的二氧化碳排放。据《麻省理工科技评论》报道，训练深度学习算法的排放量几乎是普通美国汽车在其整个生命周期（包括制造）中排放量的五倍。一个训练下棋的人工智能系统可以产生19.2万磅的二氧化碳。如果我们不重新评估当前的人工智能研究议程，人工智能可能会在未来几年加剧气候变化，并严重污染地球。该提案的总体目标是通过优化流程的各个方面来减少人工智能数据中心（DCs）的能耗。这些变化从计算硬件和软件算法开始，延伸到电源。我们的目标是让数据中心为智能数据收集、处理和学习应用做好准备。医疗保健和社会科学需要新的计算基础设施和数据中心来帮助专业人员提高这些关键方面的质量。我们需要结合并扩大我们在电力电子、数学、深度学习、生物医学、社会科学、化学工程等领域的知识和成果，为人工智能数据中心建立高效、自主的基础设施。为实现这一目标，我们将重点关注：i)用清洁和可再生能源为人工智能数据中心供电；Ii)优化算法和数据管理方法，降低能耗；iii)为AI数据中心开发新型储能和电源管理基础设施。

Energy-consuming processors are widely used in rapidly developing powerful cloud-based computing and artificial intelligence (AI). AI's power consumption is enormously increased due to the rush in admiration of deep learning that processes huge amounts of data with very large mathematical models that can consist of hundreds of millions or even billions of parameters. It consumes a lot of energy and generates lots of CO2 emissions. According to MIT Technology Review, training deep-learning algorithms emits nearly five times as much emissions as the average American car throughout its entire life cycle, including manufacturing. An artificial intelligence system training to play chess can generate 192,000 pounds of CO2. If we do not reassess the current AI research agenda, AI could contribute to climate change in the years to come and significantly pollute the earth.The overarching goal of this proposal is to reduce the energy consumption of AI Data Centers (DCs) by optimizing every aspect of the procedure. The changes begin with computing hardware and software algorithms and extend to power sources. Our goal is to prepare DCs for smart data collection, processing, and learning applications. Healthcare and social sciences require new computing infrastructures and DCs to help professionals enhance the quality of these crucial aspects. We need to combine and broaden our knowledge and our achievements in power electronics, mathematics, deep learning, biomedical, social science, and chemical engineering to build high-efficiency self-reliant infrastructures for the AI DCs. To achieve this goal, we will focus on: i) powering AI DCs with clean and renewable energy sources; ii) optimizing the algorithms and data management methods to reduce energy consumption; iii) developing novel energy storage and power management infrastructure for AI DCs.