Priyanka Raina

Title: CHIMERA: Efficient DNN Training and Inference at the Edge with On-Chip Resistive RAM



Performing energy-efficient deep neural network training and inference at the edge is challenging with current memory technologies, and as neural networks grow in size and computation, this problem is getting worse. Emerging non-volatile memories may be the answer with resistive RAM (RRAM) being one of the most promising candidates. To evaluate RRAM in the context of neural network training and inference at the edge, we designed, fabricated, and tested CHIMERA, the first non-volatile edge AI SoC using foundry provided on-chip RRAM macros and no off-chip memory. CHIMERA achieves 0.92 TOPS peak performance and 2.2 TOPS/W. We scale inference to 6x larger DNNs by connecting 6 CHIMERAs with just 4% execution time and 5% energy costs, enabled by communication-sparse DNN mappings that exploit RRAM non-volatility through quick chip wakeup/shutdown. We demonstrate the first incremental edge AI training which overcomes RRAM write energy, speed, and endurance challenges. Our training achieves the same accuracy as traditional algorithms with up to 283x fewer RRAM weight update steps and 340x better energy-delay product. We thus demonstrate 10 years of 20 samples/minute incremental edge AI training on CHIMERA.


Priyanka Raina is an Assistant Professor of Electrical Engineering at Stanford University. She received her B.Tech. degree in Electrical Engineering from the IIT Delhi in 2011 and her S.M. and Ph.D. degrees in Electrical Engineering and Computer Science from MIT in 2013 and 2018. Priyanka’s research is on creating high-performance and energy-efficient architectures for domain-specific hardware accelerators in existing and emerging technologies. She also works on methodologies for agile hardware-software co-design. Her research has won best paper awards at VLSI, ESSCIRC, MICRO and JSSC. She has also won the NSF CAREER Award, the Intel Rising Star Faculty Award, Hellman Faculty Scholar Award and is a Terman Faculty Fellow.