MajorityNets: BNNs Utilising Approximate Popcount for Improved
Efficiency
- URL: http://arxiv.org/abs/2002.12900v1
- Date: Thu, 27 Feb 2020 04:02:43 GMT
- Title: MajorityNets: BNNs Utilising Approximate Popcount for Improved
Efficiency
- Authors: Seyedramin Rasoulinezhad, Sean Fox, Hao Zhou, Lingli Wang, David
Boland, Philip H.W. Leong
- Abstract summary: This paper proposes a smaller, faster, more energy-efficient approximate replacement for the XnorPopcount operation, called XNorMaj.
We show that XNorMaj is up to 2x more resource-efficient than the XnorPopcount operation.
- Score: 13.186127108769615
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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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