Accurate and Efficient Stochastic Computing Hardware for Convolutional Neural Networks

This paper presents an efficient unipolar stochastic computing hardware for convolutional neural networks (CNNs). It includes stochastic ReLU and optimized max function, which are key components in a CNN. To avoid the range limitation problem of stochastic numbers and increase the signal-to-noise ra...

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Bibliographic Details
Main Authors: Joonsang Yu, Kyounghoon Kim, Jongeun Lee, Kiyoung Choi
Format: Conference Proceeding
Language:English
Subjects:
Convolutional neural network (CNN)
Encoding
Energy consumption
energy efficiency
Hardware
Logic gates
rectified linear units (ReLU)
stochastic computing (SC)
weight modulation
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Summary:This paper presents an efficient unipolar stochastic computing hardware for convolutional neural networks (CNNs). It includes stochastic ReLU and optimized max function, which are key components in a CNN. To avoid the range limitation problem of stochastic numbers and increase the signal-to-noise ratio, we perform weight normalization and upscaling. In addition, to reduce the overhead of binary-to-stochastic conversion, we propose a scheme for sharing stochastic number generators among the neurons in a CNN. Experimental results show that our approach outperforms the previous ones based on stochastic computing in terms of accuracy, area, and energy consumption.
ISSN:1063-6404
2576-6996
DOI:10.1109/ICCD.2017.24