An Efficient Multilayer Spiking Convolutional Neural Network Processor for Object Recognition With Low Bitwidth and Channel-Level Parallelism

Previous studies have shown that the event-driven multilayer spiking convolutional neural network (SCNN) can reduce computational complexity largely while keeping accurate. To fully utilize the advantages of SCNN, this brief proposed an efficient multilayer SCNN processor for object recognition. The...

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Vydáno v:	IEEE transactions on circuits and systems. II, Express briefs Ročník 69; číslo 12; s. 5129 - 5133
Hlavní autoři:	Feng, Lichen, Zhang, Yueqi, Zhu, Zhangming
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.12.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Artificial neural networks Channel-level parallel Convolutional neural networks Encoding Image classification Microprocessors Multilayers Neurons Nonhomogeneous media Object recognition Parallel operation Random access memory rank-order coding sparse event spike-centric Spiking spiking convolutional neural network Support vector machines
ISSN:	1549-7747, 1558-3791
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Abstract	Previous studies have shown that the event-driven multilayer spiking convolutional neural network (SCNN) can reduce computational complexity largely while keeping accurate. To fully utilize the advantages of SCNN, this brief proposed an efficient multilayer SCNN processor for object recognition. The interconnection between spiking layers is implemented for the first time. The rank-order coding with mutual and lateral inhibitions enables sparse event transmission. By further combining the spike-centric membrane potential update, channel-level parallel operation, and the low bitwidths of synapse weights and potentials, the proposed design achieves 500 classifications/s, and 68 uJ/classification for recognizing images with <inline-formula> <tex-math notation="LaTeX">160\mathbf {\times }250 </tex-math></inline-formula> resolution, which is superior to the recent works.
AbstractList	Previous studies have shown that the event-driven multilayer spiking convolutional neural network (SCNN) can reduce computational complexity largely while keeping accurate. To fully utilize the advantages of SCNN, this brief proposed an efficient multilayer SCNN processor for object recognition. The interconnection between spiking layers is implemented for the first time. The rank-order coding with mutual and lateral inhibitions enables sparse event transmission. By further combining the spike-centric membrane potential update, channel-level parallel operation, and the low bitwidths of synapse weights and potentials, the proposed design achieves 500 classifications/s, and 68 uJ/classification for recognizing images with [Formula Omitted] resolution, which is superior to the recent works. Previous studies have shown that the event-driven multilayer spiking convolutional neural network (SCNN) can reduce computational complexity largely while keeping accurate. To fully utilize the advantages of SCNN, this brief proposed an efficient multilayer SCNN processor for object recognition. The interconnection between spiking layers is implemented for the first time. The rank-order coding with mutual and lateral inhibitions enables sparse event transmission. By further combining the spike-centric membrane potential update, channel-level parallel operation, and the low bitwidths of synapse weights and potentials, the proposed design achieves 500 classifications/s, and 68 uJ/classification for recognizing images with <inline-formula> <tex-math notation="LaTeX">160\mathbf {\times }250 </tex-math></inline-formula> resolution, which is superior to the recent works.
Author	Zhu, Zhangming Feng, Lichen Zhang, Yueqi
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SubjectTerms	Artificial neural networks Channel-level parallel Convolutional neural networks Encoding Image classification Microprocessors Multilayers Neurons Nonhomogeneous media Object recognition Parallel operation Random access memory rank-order coding sparse event spike-centric Spiking spiking convolutional neural network Support vector machines
Title	An Efficient Multilayer Spiking Convolutional Neural Network Processor for Object Recognition With Low Bitwidth and Channel-Level Parallelism
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