Raghunathan - 2023 Arden L. Bement Jr. Award
2023 Arden L. Bement Jr. Distinguished Lecture
AI’s Energy Challenge and four A’s to Address it
Biography
Anand Raghunathan, the Silicon Valley Professor of Electrical and Computer Engineering, has been chosen to receive Purdue’s 2023 Arden L. Bement Jr. Award. The award is given annually to a university researcher who has made highly significant and impactful contributions to pure and applied sciences and engineering. Raghunathan is being recognized for his pioneering work in making artificial intelligence (AI) systems more energy-efficient through specialized hardware architectures for AI workloads and the design paradigm of approximate computing.
A fellow of the Institute of Electrical and Electronics Engineers and the Association for Computing Machinery, Raghunathan has been a Purdue faculty member since 2008. He is a founding codirector of the Purdue-led Center for a Secured Microelectronics Ecosystem and codirector of the Center for the Co-design of Cognitive Systems funded by the Semiconductor Research Corp. and the U.S. Defense Advanced Research Projects Agency.
“Anand was one of the very first researchers to realize that machine learning and data analytics would drive the future of computing platforms and their underlying hardware fabrics. His group created some of the first hardware accelerators for AI workloads, in the process recognizing the need for new design paradigms to create such hardware,” said Kaushik Roy, the Edward G. Tiedemann Jr. Distinguished Professor of Electrical and Computer Engineering, in nominating Raghunathan. “This led him to his pioneering work in approximate computing, which has deeply influenced subsequent efforts in academia and industry and has been recognized with best paper and test-of-time awards.”
“I am deeply honored to be chosen to receive this award,” Raghunathan said. “I am indebted to all my mentors, collaborators and students over the years, and to Purdue for providing an amazing environment in which to pursue my research.”
Looking ahead to future challenges, Raghunathan has clearly defined his priorities: “Artificial Intelligence has fundamentally altered the trajectory of demand for computing. Our ability to address the AI compute efficiency challenge will shape the future of AI and many other fields. I hope to tackle this challenge through my work.”
“Prof. Raghunathan’s pioneering research in utilizing approximate computing techniques for improving the efficiency of AI hardware — specifically, trained quantization of deep neural networks — has been foundational to enabling transformative advances in AI systems in recent years across the industry,” said Vivek De, Intel fellow and director of circuit technology research.
Among many accolades, Raghunathan was cited as one of the world's top 35 innovators under the age of 35 by MIT Technology Review magazine. He has received nine best paper awards, a ten-year retrospective most influential paper award and a best design contest award at premier conferences in his field. Before joining Purdue, he received a Patent of the Year Award and two Technology Commercialization Awards from NEC Corp. for his work that shaped multiple generations of semiconductor products. At Purdue, he has received the College of Engineering Faculty Excellence Award for Research, the Qualcomm Faculty Award and the IBM Faculty Award.
Abstract
Artificial Intelligence is transforming the landscape of computing and in turn several facets of our lives. Creating or using an AI model has an important, and often invisible, by-product—energy consumption in the underlying computing system that the model runs on. The meteoric improvements in AI systems that we have witnessed over the past decade have been accompanied by equally rapid increases in their appetite for energy. Is this trend sustainable? How can the computing industry ensure that progress in AI is not gated by energy costs? We will explore the energy footprint of today's AI systems and discuss four A’s—Awareness, Algorithms, Application-specific hardware, and Approximate computing—that can pave the way towards a future with energy-efficient AI.
