  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.
