Experimental Demonstration of Array-level Learning with Phase Change Synaptic Devices

IBM Research, T.J. Watson Research Center, Yorktown Heights, NY Abstract The computational performance of the biological brain has long attracted significant interest and has led to inspirations in operating principles, algorithms, and architectures for computing and signal processing. In this work, we focus on hardware implementation of brain-like learning in a brain-inspired architecture. We demonstrate, in hardware, that 2-D crossbar arrays of phase change synaptic devices can achieve associative learning and perform pattern recognition. Device and array-level studies using an experimental 10 10 array of phase change synaptic devices have shown that pattern recognition is robust against synaptic resistance variations and large variations can be tolerated by increasing the number of training iterations. Our measurements show that increase in initial variation from 9 % to 60 % causes required training iterations to increase from 1 to 11. I. Introduction Synaptic electronics is an emerging field of research aiming to build electronic systems that mimic computational energyefficiency and fault tolerance of biological brain in a compact space [1]. Figure 1: Left figure is a DSI (diffusion spectrum imaging) scan showing a fabric-like 3-D grid structure of connections in the monkey brain (Credit: Van Wedeen, M.D., Martinos Center and Dept. of Radiology, Massachusetts General Hospital and Harvard University Medical School) [6].