基于随机存储器的加权脉冲神经网络模拟神经元设计

2019 International SoC Design Conference (ISOCC) Pub Date : 2019-10-06 DOI:10.1109/ISOCC47750.2019.9078507

Chaeun Lee, Jaehyun Kim, Kiyoung Choi

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引用次数: 0

摘要

尖峰神经网络(snn)很有前途，因为它们能够用具有相同高度的简单尖峰序列来表示信号强度信息。在本文中，我们提出了一种基于随机存储器的模拟神经元电路用于加权尖峰神经网络，该电路节能且硬件友好。我们设计的神经元电路表明，加权尖峰神经网络可以在模拟中实现，并且工作正常。

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An RRAM-based Analog Neuron Design for the Weighted Spiking Neural network

Spiking neural networks (SNNs) are promising because they have the ability to represent signal strength information with a simple sequence of spikes having the same height. In this paper, we propose an RRAM-based analog neuron circuit for the weighted spiking neural network which is energy-efficient and hardware-friendly. We have designed the neuron circuit to show that the weighted spiking neural network can be implemented in analog and works properly.

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来源期刊

2019 International SoC Design Conference (ISOCC)

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