NullaNet Tiny: Ultra-low-latency DNN Inference Through Fixed-function Combinational Logic

Abstract

While there is a large body of research on efficient processing of deep neural networks (DNNs) [1]-[31], ultra-low-latency realization of these models for applications with stringent, sub-microsecond latency requirements continues to be an unresolved, challenging problem. Field-programmable gate array (FPGA)-based DNN accelerators are gaining traction as a serious contender to replace graphics processing unit/central processing unit-based platforms considering their performance, flexibility, and energy efficiency. NullaNet (2018) [32], LUTNet (2019) [33], and LogicNets (2020) [34] are among accelerators specifically designed to benefit from FPGAs' capabilities.