A Hybrid Architecture for Efficient FPGA-based Implementation of Multilayer Neural Network

This paper presents a novel architecture for the FPGA-based implementation of multilayer neural network (NN), which integrates the layer-multiplexing and pipeline architecture together. The proposed method is aimed at enhancing the efficiency of resource usage and improving the forward speed at the module level, so that a larger NN can be implemented on commercial FPGAs. We developed a mapping method from NN schematic to physical architecture in FPGA by using the hybrid architecture, and also developed an algorithm to automatically determine the architecture by optimizing the application specific neural network topology. The experimental results with several different network topologies show that the proposed architecture can produce a very compact circuit with higher speed, compared with conventional methods.