Attention Augmented Convolutional Neural Network for Fine-Grained Plant Disease Classification and Visualization Using Stochastic Sample Transformations

Yilma, Getinet; Qin, Zhiguang; Assefa, Maregu; Alemu, G; Ayalew, Melese

doi:10.1145/3502827.3502836

Cited by 1 publication

(1 citation statement)

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“…Han et al proposed an Energy Efficient Inference Engine (EIE), which utilized weight sharing and distributed storage computing to accelerate neural networks, showing obvious advantages in energy consumption and hardware area reduction [171]. Ren et al adopted a top-down approach to design an optimization framework for SC-based DNNs, fully using SC's advantages, significantly reducing the hardware area and achieving low power consumption while maintaining high network accuracy [172].…”

Section: A Application Of Probabilistic Computing In Traditional Neur...mentioning

confidence: 99%

A Survey of Intelligent Chip Design Research Based on Spiking Neural Networks

2022

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The traditional neural network Intelligent chip has the problem of high power consumption due to classical computing architecture, limiting the development of neural network Intelligent chips. Stochastic computing (SC) encodes binary numbers into stochastic pulse sequences in operation, taking advantage of low power consumption and high performance. The application of SC in spiking neural networks (SNNs) Intelligent chips is beneficial to solving the high power consumption of traditional neural network chips. This article first summarizes the basic elements of SNNs and the basic principles of SC. Then, we review the development trends of the stochastic computation-based neural network chips and existing SNN chips under research at home and abroad, respectively, and analyze the current problems. Finally, a review of SNN chips based on SC is highlighted. This paper aims to provide new research directions and to learn ideas for the field of SNN chips through systematic summaries.

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Section: A Application Of Probabilistic Computing In Traditional Neur...mentioning

confidence: 99%