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Low-Power Computing Unit based on Heterogeneous Approximate Structure for Binary Convolutional Neural Network | IEEE Conference Publication | IEEE Xplore

Low-Power Computing Unit based on Heterogeneous Approximate Structure for Binary Convolutional Neural Network


Abstract:

This paper proposes a low-power computing unit based on heterogeneous approximate structure, which is suitable for deployment in binary convolutional neural network (BCNN...Show More

Abstract:

This paper proposes a low-power computing unit based on heterogeneous approximate structure, which is suitable for deployment in binary convolutional neural network (BCNN). We propose a heterogeneous approximate structure based on approximate adders, which reduces the hardware resources consumption of BCNN deployment without loss of accuracy through error inter-compensation of different adders. The proposed low-power computing unit based on this heterogeneous structure can complete convolution operations in BCNN and greatly reduce power consumption. Implemented under 22nm ULL process, compared with the standard accurate computing unit, the proposed computing unit can reduce the power consumption by 33.92% and delay by 46.36%, while reducing the accuracy by less than 1% in GSCD.
Date of Conference: 25-28 October 2022
Date Added to IEEE Xplore: 01 December 2022
ISBN Information:
Conference Location: Nangjing, China

References

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