基于仿真的神经元电路有效比较分析,用于神经形态计算系统

IF 5.5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Deepthi M.S. , Shashidhara H.R. , Jayaramu Raghu , Rudraswamy S.B.
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引用次数: 0

摘要

尖峰神经网络(SNN)的灵感来源于人脑,具有脑级计算能力、功耗低、数据移动成本小等优点,为神经形态计算系统的发展提供了更广阔的应用空间。基于尖峰的神经元和突触是 SNN 的基本构件,它们的高效实现对提高 SNN 的性能至关重要。在这方面,尖峰神经元的设计和实现一直是研究人员关注的重点。本文比较了基于不同漏电集成火花(LIF)的尖峰神经元电路的功能,如基于 CMOS 的频率自适应 LIF、基于电阻电容(RC)的 LIF 和基于易失性 Memristor 的 LIF。工作主要集中在对上述神经元电路的尖峰持续时间和振幅、激励期间产生的尖峰数量、阈值操作、应用领域和其他各种重要参数进行揭示分析。研究利用 Cadence Virtuoso 仿真环境对这些电路进行了大量仿真,以验证其行为。此外,考虑到电路复杂性、电源电压、发射率、膜电容、输入/输出性质、耐火机制和每个尖峰的能耗等属性,还进行了简要的比较分析。这项工作旨在帮助研究人员选择合适的 LIF 模型,以便针对特定应用高效地构建基于忆阻器和/或非忆阻器的 SNN。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Simulation-based effective comparative analysis of neuron circuits for neuromorphic computation systems
The spiking neural networks (SNN) that are inspired by the human brain offers wider scope for application in the growth of neuromorphic computing systems due to their brain level computational capabilities, reduced power consumption, and minimal data movement cost, among other advantages. Spike-based neurons and synapses are the essential building blocks of SNN, and their efficient implementation is vital to their performance enhancement. In this regard, the design and implementation of spiking neurons have been the major focus among the researchers. In this paper, functioning of different leaky integrate fire (LIF)-based spiking neuron circuits like frequency adaptable CMOS-based LIF, resistor-capacitor-based (RC) LIF, and volatile memristor-based LIF are subjected to comparison. The work mainly focuses on revealing analysis of spike duration and amplitude, number of spikes produced during excitation period, threshold operation, field of application, and various other significant parameters of aforementioned neuron circuits. Extensive simulations of these circuits are carried out utilizing the Cadence Virtuoso simulation environment in order to validate their behavior. Further, a brief comparative analysis is executed considering into account the attributes like circuit complexity, supply voltage, firing rate, membrane capacitance, nature of input/output, refractory mechanism, and energy consumption per spike. This work seeks to assist researchers in selecting an appropriate LIF model to efficiently construct memristors and/or non-memristors based SNN for certain application.
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来源期刊
Neurocomputing
Neurocomputing 工程技术-计算机:人工智能
CiteScore
13.10
自引率
10.00%
发文量
1382
审稿时长
70 days
期刊介绍: Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.
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