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arXiv · 2002.12900

MajorityNets: BNNs Utilising Approximate Popcount for Improved Efficiency

Seyedramin Rasoulinezhad·Sean Fox·Hao Zhou·Lingli Wang·David Boland·Philip H. W. Leong
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Abstract

Binarized neural networks (BNNs) have shown exciting potential for utilising neural networks in embedded implementations where area, energy and latency constraints are paramount. With BNNs, multiply-accumulate (MAC) operations can be simplified to XnorPopcount operations, leading to massive reductions in both memory and computation resources. Furthermore, multiple efficient implementations of BNNs have been reported on field-programmable gate array (FPGA) implementations. This paper proposes a smaller, faster, more energy-efficient approximate replacement for the XnorPopcountoperation, called XNorMaj, inspired by state-of-the-art FPGAlook-up table schemes which benefit FPGA implementations. Weshow that XNorMaj is up to 2x more resource-efficient than the XnorPopcount operation. While the XNorMaj operation has a minor detrimental impact on accuracy, the resource savings enable us to use larger networks to recover the loss.

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Seyedramin Rasoulinezhad, Sean Fox, Hao Zhou, Lingli Wang, David Boland, Philip H. W. Leong. 2020-02-27. MajorityNets: BNNs Utilising Approximate Popcount for Improved Efficiency. https://doi.org/10.1109/icfpt47387.2019.00062

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