A Fast Weight Transfer Method for Real-Time Online Learning in RRAM-Based Neuromorphic System

Min-Hwi Kim,Byung-Gook Park,Sin-Hyung Lee,Sungjun Kim

doi:10.1109/access.2022.3157333

Abstract

In this work, a synaptic weight transfer method for a neuromorphic system based on resistive-switching random-access memory (RRAM) is proposed and validated. To implement the on-chip trainable neuromorphic system which utilizes large-scale hardware synapse units, a fast and reliable write scheme needs to be established. Based on the experimental results, it is confirmed that the gradual set and full reset operation is the most suitable operation scheme for fast programming due to the fundamental reliability characteristics of the resistive-switching memory cell. Also, the superiority of this programming method using the proposed RRAM compact model is demonstrated. In addition, a one weight/one synaptic device structure is newly adopted for realizing high-density synapse arrays by using a nonnegative weight constraint in supervised learning. Finally, the pattern recognition accuracies obtained at the software and hardware levels are compared.

Highlights

Numerous studies have been conducted in academia and industry to imitate the limitless cognitive abilities of the human brain to learn, remember, infer, and forget in an incredibly energy-efficient and natural way [1]
Biological neurons that operate based on the integrate-and-fire mechanism to transmit weighted signals through the synapse region and the synaptic connections and their long-/short-term plasticity are known to play the most important role in the learning and memory functions of a human brain and various studies which implement those functionalities into electronic systems have been reported [5-11]
The total time required for the overall write operation of the entire array when adopting the proposed sequence and the gradual set and full reset (GSFR)/full set and gradual reset (FSGR) operation can be expressed as follows: In these equations, nWL is the number of word lines, nstate is the number of conductivity states, and ɑ is the number of pulses to increase on the conductivity state

Summary

Introduction

Numerous studies have been conducted in academia and industry to imitate the limitless cognitive abilities of the human brain to learn, remember, infer, and forget in an incredibly energy-efficient and natural way [1]. INDEX TERMS neuromorphic, hardware-driven artificial intelligence, synaptic device, weight transfer, resistive-switching random-access memory (RRAM), artificial neural network (ANN), cross-point array architecture.

Results

Conclusion