一种适用于脉冲神经网络的超阈值紧凑硅神经元电路

2022 IEEE International Conference of Electron Devices Society Kolkata Chapter (EDKCON) Pub Date : 2022-11-26 DOI:10.1109/EDKCON56221.2022.10032919

Sayantan Samanta, Koushik Naskar, Souvanik Pal, Suman Mallik, Sudipta Ghosh, Swarnil Roy

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引用次数: 0

摘要

在本工作中，我们设计并模拟了一个紧凑的加速时间硅神经元电路，在超阈值下实现适当的神经动力学。利用该电路，我们精确地模拟了规则尖峰(RS)、快速尖峰(FS)、抖振(CH)和固有爆裂(IB)。该电路的功能在32nm、45nm和22nm FinFET技术下进行了极限仿真验证。对于这种电路，我们分析了不同的比较器电路来满足所需的动态，并得出阈值修正比较器最适合我们的目的。

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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A super Threshold Compact Silicon Neuron Circuit for Different Neuron Dynamics Suitable for Spiking Neural Network

In this work we have designed and simulated a compact accelerated time silicon neuron circuit in super threshold to achieve proper neural dynamics. With this circuit we have accurately modeled regular spiking (RS), fast spiking (FS), chattering (CH) and intrinsic bursting (IB). The functionality of this circuit is verified with extreme simulation in 32nm, 45nm and 22nm FinFET technology. For this circuits we have analyzed different comparator circuits to meet the required dynamics and concluded that threshold modified comparator serves our purpose best.

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2022 IEEE International Conference of Electron Devices Society Kolkata Chapter (EDKCON)

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