A Neuromorphic Computing System for Bitwise Neural Networks Based on ReRAM Synaptic Array

• Published in: 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS) pp. 1 - 4
• Main Authors: Li, Pin-Yi, Liu, Ren-Shuo, Chang, Meng-Fan, Tang, Kea-Tiong, Yang, Cheng-Han, Chen, Wei-Hao, Huang, Jian-Hao, Wei, Wei-Chen, Liu, Je-Syu, Lin, Wei-Yu, Hsu, Tzu- Hsiang, Hsieh, Chih-Cheng
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2018
• Subjects: Arrays; bitwise neural network; Computational modeling; customized training scheme; Field programmable gate arrays; Neural networks; neuromorphic computing system; Neuromorphics; Neurons; ReRAM synaptic array; Training
• DOI: 10.1109/BIOCAS.2018.8584810

Recent advances in neuromorphic computing system have shown resistive random-access memory (ReRAM) can be used to efficiently implement compact parallel computing arrays, which are inherently suitable for neural networks that require large amounts of matrix-vector multiplications (MVMs). In this work, we proposed a neuromorphic computing system based on ReRAM synaptic array to implement bitwise neural networks. The system contains a ReRAM synaptic array for parallel computation of bitwise MVMs, and a field-programmable gate array for data buffering and processing. To deploy the network on the system, a customized training scheme was required to adapt the trained network to the characteristic of ReRAM synaptic array with bitwise weights and inputs. We also managed the resolution of partial sum to reduce the bit width requirement of sense amplifier, thereby reducing power consumption. The measurement results show that the ReRAM synaptic array consumed only 0.27mW at 1V supply by using 1-bit sense amplifier while the system still maintained 97.52% accuracy on MNIST dataset.