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

点击翻译按钮获取中文摘要

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.