FPGA Architecture Enhancements for Efficient BNN Implementation

Abstract

Binarized neural networks (BNNs) are ultrareduced precision neural networks, having weights and activations restricted to single-bit values. BNN computations operate on bitwise data, making them particularly amenable to hardware implementation. In this paper, we first analyze BNN implementations on contemporary commercial 20nm FPGAs. We then propose two lightweight architectural changes that significantly improve the logic density of FPGA BNN implementations. The changes involve incorporating additional carry-chain circuitry into logic elements, where the additional circuitry is connected in a specific way to benefit BNN computations. The architectural changes are evaluated in the context of state-of-the-art Intel and Xilinx FPGAs and shown to provide over 2 area reduction in the key BNN computational task (the XNOR-popcount sub-circuit), at a modest performance cost of less than 2%.