NoD: A Neural Network-Over-Decoder for Edge Intelligence

Abstract

The ubiquitous applications of the Internet of Things (IoT) devices and the increasing computational capabilities of neural networks (NNs) have led to a new era of edge computing and a paradigm known as edge intelligence (EI). With EI, the goal is to maximize the utilization of resources available within an edge device, offloading only the most compute-intensive operations to the cloud. In this article, we propose to leverage the close similarity between the internal architecture of a typical network decoder and an NN for deep learning on decoders. The proposed NN-over-decoder is developed in Verilog and synthesized on field-programmable gate array (FPGA). Based on experimental results, the system exhibits power consumption of the same order of magnitude as a baseline decoder and negligible memory overhead while increasing local hardware utilization, alleviating the high communication load in typical communication devices, and offering scalable multiply–accumulate (MAC)/cycle performance compared with the state of the art.