Ma, Y.; Xu, Q.; Song, Z. Resource-Efficient Optimization for FPGA-Based Convolution Accelerator. Preprints2023, 2023071705. https://doi.org/10.20944/preprints202307.1705.v1
APA Style
Ma, Y., Xu, Q., & Song, Z. (2023). Resource-Efficient Optimization for FPGA-Based Convolution Accelerator. Preprints. https://doi.org/10.20944/preprints202307.1705.v1
Chicago/Turabian Style
Ma, Y., Qican Xu and Zerui Song. 2023 "Resource-Efficient Optimization for FPGA-Based Convolution Accelerator" Preprints. https://doi.org/10.20944/preprints202307.1705.v1
Abstract
Convolution forms one of the most essential operations for the FPGA-based hardware accelerator. However, the existing designs often neglect the inherent architecture of FPGA, which puts forward an austere challenge on hardware resource requirements. Even though some previous works have proposed approximate multipliers or convolution acceleration algorithms to deal with this issue, the inevitable accuracy loss and resource occupation easily lead to performance degradation. Toward this, we first propose two kinds of resource-efficient optimized accurate multipliers based on LUTs or carry chains. Then targeting FPGA-based platforms, a generic multiply-accumulate structure is constructed by directly accumulating the partial products produced by our proposed optimized radix-4 Booth multipliers without intermediate multiplication and addition results. Experimental results demonstrate that our proposed multiplier achieves a maximum 51% look-up-table (LUT) reduction compared to the Vivado area optimized multiplier IP. Furthermore, the convolutional process unit using the proposed structure achieves a 36% LUT reduction compared to existing methods. As case studies, the proposed method is applied to DCT transformer and LeNet to achieves hardware resource saving without loss of accuracy.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
