论文标题
多数网络：BNNS利用近似爆炸案以提高效率
MajorityNets: BNNs Utilising Approximate Popcount for Improved Efficiency
论文作者
论文摘要
二进制神经网络（BNN）在嵌入式实现中使用神经网络显示出令人兴奋的潜力，在嵌入式实现中，区域，能量和潜伏期约束至关重要。使用BNN，可以将多元蓄电（MAC）操作简化为Xnorpopcount操作，从而大大减少内存和计算资源。此外，已经在现场可编程栅极阵列（FPGA）实现中报告了BNN的多个有效实现。本文提出了一个较小，更快，更节能的近似替代Xnorpopcounteration，称为Xnormaj，灵感来自最先进的FPGALOOK-UP-UP台式方案，这些方案使FPGA实现受益。 Xnormaj比Xnorpopcount操作高2倍的资源效率高2倍。尽管XNORMAJ操作对准确性有很小的有害影响，但资源节省使我们能够使用较大的网络来恢复损失。
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.
