A Memristive Spiking Neural Network Circuit with Selective Supervised Attention Algorithm

Spiking neural networks (SNNs) are biologically plausible and computationally powerful. The current computing systems based on the von Neumann architecture are almost the hardware basis for the implementation of SNNs. However, performance bottlenecks in computing speed, cost, and energy consumption...

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Vydáno v:	IEEE transactions on computer-aided design of integrated circuits and systems Ročník 42; číslo 8; s. 1
Hlavní autoři:	Deng, Zekun, Wang, Chunhua, Lin, Hairong, Sun, Yichuang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.08.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Annotations Biological neural networks Circuit design Coding Datasets Encoding Energy consumption Hardware image classification Machine learning memristor Memristors Neural networks Neurons Selective attention sequence learning Spiking spiking neural network supervised algorithm Supervised learning Synapses
ISSN:	0278-0070, 1937-4151
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Abstract	Spiking neural networks (SNNs) are biologically plausible and computationally powerful. The current computing systems based on the von Neumann architecture are almost the hardware basis for the implementation of SNNs. However, performance bottlenecks in computing speed, cost, and energy consumption hinder the hardware development of SNNs. Therefore, efficient non-Neumann hardware computing systems for SNNs remain to be explored. In this paper, a selective supervised algorithm for spiking neurons inspired by the selective attention mechanism is proposed, and a memristive spiking neuron circuit as well as a memristive SNN circuit based on the proposed algorithm are designed. The memristor realizes the learning and memory of the synaptic weight. The proposed algorithm includes a top-down selective supervision method and a bottom-up selective supervision method. Compared with other supervised algorithms, the proposed algorithm has excellent performance on sequence learning. Moreover, top-down and bottom-up attention encoding circuits are designed to provide the hardware foundation for encoding external stimuli into top-down and bottom-up attention spikes, respectively. The proposed memristive SNN circuit can perform classification on the MNIST dataset and the Fashion-MNIST dataset with superior accuracy after learning a small number of labeled samples, which greatly reduces the cost of manual annotation and improves the supervised learning efficiency of the memristive SNN circuit.
AbstractList	Spiking neural networks (SNNs) are biologically plausible and computationally powerful. The current computing systems based on the von Neumann architecture are almost the hardware basis for the implementation of SNNs. However, performance bottlenecks in computing speed, cost, and energy consumption hinder the hardware development of SNNs. Therefore, efficient non von Neumann hardware computing systems for SNNs remain to be explored. In this article, a selective supervised algorithm for spiking neurons (SNs) inspired by the selective attention mechanism is proposed, and a memristive SN circuit as well as a memristive SNN circuit based on the proposed algorithm are designed. The memristor realizes the learning and memory of the synaptic weight. The proposed algorithm includes a top-down (TD) selective supervision method and a bottom-up (BU) selective supervision method. Compared with other supervised algorithms, the proposed algorithm has excellent performance on sequence learning. Moreover, TD and BU attention encoding circuits are designed to provide the hardware foundation for encoding external stimuli into TD and BU attention spikes, respectively. The proposed memristive SNN circuit can perform classification on the MNIST dataset and the Fashion-MNIST dataset with superior accuracy after learning a small number of labeled samples, which greatly reduces the cost of manual annotation and improves the supervised learning efficiency of the memristive SNN circuit. Spiking neural networks (SNNs) are biologically plausible and computationally powerful. The current computing systems based on the von Neumann architecture are almost the hardware basis for the implementation of SNNs. However, performance bottlenecks in computing speed, cost, and energy consumption hinder the hardware development of SNNs. Therefore, efficient non-Neumann hardware computing systems for SNNs remain to be explored. In this paper, a selective supervised algorithm for spiking neurons inspired by the selective attention mechanism is proposed, and a memristive spiking neuron circuit as well as a memristive SNN circuit based on the proposed algorithm are designed. The memristor realizes the learning and memory of the synaptic weight. The proposed algorithm includes a top-down selective supervision method and a bottom-up selective supervision method. Compared with other supervised algorithms, the proposed algorithm has excellent performance on sequence learning. Moreover, top-down and bottom-up attention encoding circuits are designed to provide the hardware foundation for encoding external stimuli into top-down and bottom-up attention spikes, respectively. The proposed memristive SNN circuit can perform classification on the MNIST dataset and the Fashion-MNIST dataset with superior accuracy after learning a small number of labeled samples, which greatly reduces the cost of manual annotation and improves the supervised learning efficiency of the memristive SNN circuit.
Author	Deng, Zekun Wang, Chunhua Lin, Hairong Sun, Yichuang
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SubjectTerms	Algorithms Annotations Biological neural networks Circuit design Coding Datasets Encoding Energy consumption Hardware image classification Machine learning memristor Memristors Neural networks Neurons Selective attention sequence learning Spiking spiking neural network supervised algorithm Supervised learning Synapses
Title	A Memristive Spiking Neural Network Circuit with Selective Supervised Attention Algorithm
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