Chong, Y. S., Goh, W. L., Ong, Y. S., Nambiar, V. P., & Do, A. T. (2021). An Energy-Efficient Convolution Unit for Depthwise Separable Convolutional Neural Networks. 2021 IEEE International Symposium on Circuits and Systems (ISCAS). https://doi.org/10.1109/iscas51556.2021.9401192
Abstract:
High performance but computationally expensive Convolutional Neural Networks (CNNs) require both algorithmic and custom hardware improvement to reduce model size and to improve energy efficiency for edge computing applications. Recent CNN architectures employ depthwise separable convolution to reduce the total number of weights and MAC operations. However, depthwise separable convolution workload does not run efficiently in existing CNN accelerators. This paper proposes an energy-efficient CONV unit for pointwise and depthwise operation. The CONV unit utilizes weight stationary to enable high efficiency. The row partial sum reduction is engaged to increase parallelism in pointwise convolution thereby lightening the memory requirements on output partial sums. Our design achieves a maximum efficiency of 3.17 TOPS/W at 0.85V/40nm CMOS which is well-suited for energy constrained edge computing applications.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - RIE2020 Advanced Manufacturing and Engineering Domain (AME)
Grant Reference no. : A1687b0033