ECE Department Seminar

AI Democratization through the Co-Design of Neural Network Architectures and Hardware Accelerators —— from Classical to Quantum Computing

Weiwen Jiang, Ph.D.,
Department of Computer Science and Engineering
University of Notre Dame

Wednesday, February 17, 2021
10:00 am – 11:00 am
Zoom Webinar Link:
https://gmu.zoom.us/j/93179914942

Abstract

Since the "AI democratization" concept was proposed in 2017, researchers have devoted themselves to push forward its progress to enter the AI democratization era, where everyone in every field can enjoy the power of AI. In the early stage, a conventional design flow that separates the optimizations of AI model and hardware acceleration has demonstrated the possibility of integrating AI to different platforms, varying from edge to cloud; however, due to the software and hardware optimizations are highly coupled, such a separate optimization will lead to the inferior solutions. In 2019, we proposed the first co-design framework to bridge these two design spaces by taking FPGA as a vehicle and using Neural Architecture Search as the fundamental search engine. Then, we extend the co-design framework to support other hardware platforms, including Cloud GPU, Mobile GPU, Computing-in-Memory, and ASIC. Most recently, we apply the co-design philosophy to quantum computing and demonstrate the quantum advantages for neural networks for the first time. In this talk, we will present our co-design methodology, which can be applied for computing platforms from classical computing to quantum computing. Our results have shown great potential for delivering the best neural networks and hardware accelerator designs to support AI democratization. The co-design frameworks for FPGA and ASICs obtained the Best Paper Nomination in DAC'19, CODES+ISSS'19, and ASP-DAC'20, and the co-design framework for quantum computer has been published at Nature Communications.

Bio

Dr. Weiwen Jiang is currently a Post-Doctoral Research Associate at the University of Notre Dame. He received the Ph.D. degree from Chongqing University in 2019. From 2017 to 2019, he was a research scholar in the Department of Electrical and Computer Engineering at the University of Pittsburgh. His research interest is on hardware and software co-design; in particular, the co-design of neural networks and different hardware accelerators, including mobile devices, FPGA, and ASIC. Most recently, he moves to the co-design of neural networks and quantum circuits. His work demonstrates the quantum advantages for neural networks for the first time, which has been published at Nature Communications. His research works have been published in prestigious journals and conferences, including Nature Electronics, Nature Communications, IEEE/ACM Transactions, DAC, ICCAD, ESWEEK, etc. He is the receipt of Best Paper Award in ICCD’17 and Best Paper Nominations in DAC'19, CODES+ISSS'19, ASP-DAC'16, and ASP-DAC'20. Last year, he received over $250,000 research funds from NSF and Industry, including Facebook and Edgecotix Inc.