TY - JOUR
T1 - Hardware Architecture of Stochastic Computing Neural Network
AU - Chen, Yuhao
AU - Song, Yinjie
AU - Zhu, Yanan
AU - Gao, Yunfei
AU - Li, Hongge
N1 - Publisher Copyright:
© 2021, Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.
PY - 2021
Y1 - 2021
N2 - Stochastic computing is a kind of logic calculation that converts binary into probabilistic coded digital pulse stream. At the cost of computing power and time delay, it has the computing advantages of low power consumption and high energy efficiency. In this paper, the basic concept of stochastic computing is explained, and a stochastic computing circuit with single-channel or multi-channel is designed to improve the speed and accuracy. Based on the stochastic computing circuit, the stochastic pulse neuron is designed, and the reconfigurable computing architecture of neural network, BUAA-ChouSuan, is realized. The design is implemented with KINTEX-7 (FPGA), the logic resource (lookup table, LUT) of stochastic MAC (multiply accumulate) is 80% lower than that of traditional MAC. In SCNN (stochastic convolutional neural network) experiment, LeNet and AlexNet are tested. Under the condition of 350 MHz clock frequency, the average energy efficiency can reach 0.536 TSOPS/W, and the utilization rate of processing unit (PE) can reach more than 90%.
AB - Stochastic computing is a kind of logic calculation that converts binary into probabilistic coded digital pulse stream. At the cost of computing power and time delay, it has the computing advantages of low power consumption and high energy efficiency. In this paper, the basic concept of stochastic computing is explained, and a stochastic computing circuit with single-channel or multi-channel is designed to improve the speed and accuracy. Based on the stochastic computing circuit, the stochastic pulse neuron is designed, and the reconfigurable computing architecture of neural network, BUAA-ChouSuan, is realized. The design is implemented with KINTEX-7 (FPGA), the logic resource (lookup table, LUT) of stochastic MAC (multiply accumulate) is 80% lower than that of traditional MAC. In SCNN (stochastic convolutional neural network) experiment, LeNet and AlexNet are tested. Under the condition of 350 MHz clock frequency, the average energy efficiency can reach 0.536 TSOPS/W, and the utilization rate of processing unit (PE) can reach more than 90%.
KW - brain like chip
KW - low power consumption
KW - spiking neural network
KW - stochastic computing
UR - https://www.scopus.com/pages/publications/85168435137
U2 - 10.3778/j.issn.1673-9418.2105050
DO - 10.3778/j.issn.1673-9418.2105050
M3 - 文章
AN - SCOPUS:85168435137
SN - 1673-9418
VL - 15
SP - 2105
EP - 2115
JO - Journal of Frontiers of Computer Science and Technology
JF - Journal of Frontiers of Computer Science and Technology
IS - 11
ER -