Energy Efficient Deep Neural Network Processing-In-MRAM: From Device to Algorithm
Deliang Fan, Assistant Professor at Arizona State University
In-memory computing is becoming a promising solution to reduce massive power hungry data traffic between computing and memory units, leading to significant improvement of entire system performance and energy efficiency. Emerging spintronic device based non-volatile Magnetic RAM (MRAM) has been a next-generation high performance non-volatile memory candidate due to its non-volatility, zero leakage power in un-accessed bit-cell, high integration density, excellent endurance and compatibility with CMOS fabrication technology. In this talk, Dr. Deliang Fan will present his recent research in leveraging innovations from both post-CMOS non-volatile MRAM technology and in-memory logic circuit & architecture to intrinsically integrate memory and processing units, targeting to advance next-generation revolutionary deep neural network processing-in-MRAM paradigm, and to achieve orders higher performance and ultra-low energy consumption compared with traditional computing platforms. Such new intelligent and energy efficient processing-in-MRAM platform is also demonstrated to be able to efficiently accelerate many data/compute-intensive applications, including data encryption, image/graph processing, etc.
Dr. Deliang Fan is is currently an Assistant Professor in the School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA. Before joining ASU in 2019, he was an assistant professor in Department of Electrical and Computer Engineering at University of Central Florida, Orlando, FL, USA. He received his M.S. and Ph.D. degrees, under the supervision of Prof. Kaushik Roy, in Electrical and Computer Engineering from Purdue University, West Lafayette, IN, USA, in 2012 and 2015, respectively. He received his B.S. degree in Electronic Information Engineering from Zhejiang University, Hangzhou, China, in 2010.