Evaluation of the Medium-sized Neural Network using Approximative Computations on Zynq FPGA

Abstract

Integrating artificial intelligence technologies into embedded systems requires efficient implementation of neural networks in hardware. The paper presents a Zynq 7020 FPGA implementation and evaluation of a middle-sized dense neural network based on approximate computation by linearly approximated functions. Three famous benchmarks were used for classification accuracy evaluation and hardware testing. We use our highly pipelined neural hardware architecture that takes weights from block RAMs to save logic resources and enables their update from the processing system. The architecture reaches excellent design scalability, allowing us to estimate the number of neurons implemented in programmable logic based on single-neuron resources. We reached nearly full chip utilization while preserving the high clock freuuency for the FPGA used.