Neuromorphic devices and architectures for next-generation cognitive computing

• Published in: 2017 IEEE International Symposium on Circuits and Systems (ISCAS) pp. 1 - 4
• Main Authors: Burr, Geoffrey W., Narayanan, Pritish, Shelby, Robert M., Ambrogio, Stefano, Hsinyu Tsai, Lewis, Scott L., Hosokawa, Kohji
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2017
• Subjects: Biological neural networks; Computer architecture; Neurons; Nonvolatile memory; Phase change materials; System-on-chip; Training
• ISSN: 2379-447X
• DOI: 10.1109/ISCAS.2017.8050222

Cognitive computing describes "systems that learn at scale, reason with purpose, and interact with humans naturally" [1]. In this paper, we review our work towards enabling "next generation" cognitive computing using neuromorphic computational schemes that could potentially outperform present-day CPUs and GPUs. Here we use large arrays of Resistive Non-Volatile Memories (NVM) with device conductance serving as synaptic weight. We focus on training and classification using fully-connected networks based on the backpropagation algorithm, and show that our approach could offer power and speed advantages over conventional Von-Neumann processors. We also propose some circuit approximations that improve network parallelism without significantly degrading classification accuracy. Finally, we explore the requirements for a system implementation of on-chip learning.