High-Throughput DNN Inference with LogicNets

Yaman Umuroglu, Yash Akhauri, Nicholas J. Fraser, Michaela Blott

Abstract: Deep Neural Networks (DNNs) have a wide application scope beyond computer vision tasks, promising to replace manual algorithmic implementations in applications ranging from large-scale physics experiments to next-generation network security. Such applications may require data processing rates in the millions of samples per second and sub-microsecond latency, which is possible with customized FPGA or ASIC implementations. We present a novel method called LogicNets for co-design of DNN topologies and hardware circuits that maps to a very efficient FPGA implementation to address the needs of such applications.