Thermal Instability Compensation of Synaptic 3D Flash Memory-Based Hardware Neural Networks With Adaptive Read Bias

IF 4.1 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Electron Device Letters Pub Date : 2024-09-12 DOI:10.1109/LED.2024.3459615

Jangsaeng Kim;Jiseong Im;Jong-Ho Lee

引用次数: 0

Abstract

Ambient temperature variations caused by extensive operations in hardware neural networks (HNNs) are a critical issue that significantly degrades performance. In this work, HNNs based on synaptic 3D flash memory with high area and energy efficiency are proposed. The impact of ambient temperature on synaptic weights composed of pairs of synaptic devices was investigated. Our proposed thermal instability compensation method using adaptive read bias restores distorted outputs with a low hardware burden. The effective weight modulation with adaptive read bias successfully recovers HNN performance to near baseline even at a high ambient temperature of 100°C.

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基于自适应读取偏置的突触三维闪存硬件神经网络的热不稳定性补偿

硬件神经网络（HNN）的大量运行导致的环境温度变化是一个关键问题，会显著降低性能。本研究提出了基于突触三维闪存的高面积和高能效 HNN。研究了环境温度对成对突触器件组成的突触权重的影响。我们提出的热不稳定性补偿方法采用自适应读取偏置，以较低的硬件负担恢复失真的输出。即使在 100°C 的高环境温度下，利用自适应读取偏置的有效权重调制也能成功地将 HNN 性能恢复到接近基线的水平。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Electron Device Letters 工程技术-工程：电子与电气

CiteScore

8.20

自引率

10.20%

发文量

551

审稿时长

1.4 months

期刊介绍： IEEE Electron Device Letters publishes original and significant contributions relating to the theory, modeling, design, performance and reliability of electron and ion integrated circuit devices and interconnects, involving insulators, metals, organic materials, micro-plasmas, semiconductors, quantum-effect structures, vacuum devices, and emerging materials with applications in bioelectronics, biomedical electronics, computation, communications, displays, microelectromechanics, imaging, micro-actuators, nanoelectronics, optoelectronics, photovoltaics, power ICs and micro-sensors.