BiNPU: A 33.0 MOP/s/LUT Binary Neural Network Inference Processor Showing 88.26% CIFAR10 Accuracy With 1.9 Mbit On-Chip Parameters in a 28-nm FPGA

An efficient processor to perform inference of binary neural networks (BNNs) is presented. The proposed processor, named BiNPU, is designed based on a unified architecture that can efficiently process BNN modules of various types, including those with group convolution and global average pooling, in a consistent output-parallel mechanism without resource overhead. Implemented in a 28 nm FPGA, BiNPU shows the resource efficiency as high as 33.0 MOP/s/LUT, 35.3% higher than the previous state-of-the-art processor that supports even fewer module types. BiNPU performs the CIFAR10 classification task achieving 88.26% accuracy with 1.9 Mbit parameters entirely stored in on-chip memories. The BRAM usage for implementing the on-chip memories is rather smaller than those of the previous processors stored some of the parameters in off-chip memories.