Implementing Binarized Neural Network Processor on FPGA-Based Platform

Binarized neural networks (BNNs) have 1-bit weights and activations, which are well suited for FPGAs. The BNNs suffer from accuracy loss compared with conventional neural networks. Shortcut connections are introduced to address the performance degradation. This work proposes a BNN processor supporting the shortcut connects. To evaluate the performance of the processor, we implement the system on an FPGA (Xilinx Kintex UltraScale). Our experiments show that the proposed processor achieves state-of-the-art energy efficiency.