A Highly Robust Binary Neural Network Inference Accelerator Based on Binary Memristors

Author:

Zhao YiyangORCID,Wang Yongjia,Wang Ruibo,Rong Yuan,Jiang Xianyang

Abstract

Since memristor was found, it has shown great application potential in neuromorphic computing. Currently, most neural networks based on memristors deploy the special analog characteristics of memristor. However, owing to the limitation of manufacturing process, non-ideal characteristics such as non-linearity, asymmetry, and inconsistent device periodicity appear frequently and definitely, therefore, it is a challenge to employ memristor in a massive way. On the contrary, a binary neural network (BNN) requires its weights to be either +1 or −1, which can be mapped by digital memristors with high technical maturity. Upon this, a highly robust BNN inference accelerator with binary sigmoid activation function is proposed. In the accelerator, the inputs of each network layer are either +1 or 0, which can facilitate feature encoding and reduce the peripheral circuit complexity of memristor hardware. The proposed two-column reference memristor structure together with current controlled voltage source (CCVS) circuit not only solves the problem of mapping positive and negative weights on memristor array, but also eliminates the sneak current effect under the minimum conductance status. Being compared to the traditional differential pair structure of BNN, the proposed two-column reference scheme can reduce both the number of memristors and the latency to refresh the memristor array by nearly 50%. The influence of non-ideal factors of memristor array such as memristor array yield, memristor conductance fluctuation, and reading noise on the accuracy of BNN is investigated in detail based on a newly memristor circuit model with non-ideal characteristics. The experimental results demonstrate that when the array yield α ≥ 5%, or the reading noise σ ≤ 0.25, a recognition accuracy greater than 97% on the MNIST data set is achieved.

Funder

Fundamental Research Funds for the Central Universities

National Science Foundation of China

Natural Science Foundation of Hubei Province

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

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1. Binarized Neural Network Comprising Quasi‐Nonvolatile Memory Devices for Neuromorphic Computing;Advanced Electronic Materials;2024-05-28

2. Binarized neural network of diode array with high concordance to vector–matrix multiplication;Scientific Reports;2024-03-11

3. Multi-bit MRAM based high performance neuromorphic accelerator for image classification;Neuromorphic Computing and Engineering;2024-03-01

4. Robust Ex-situ Training of Memristor Crossbar-based Neural Network with Limited Precision Weights;Proceedings of the 18th ACM International Symposium on Nanoscale Architectures;2023-12-18

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