NS-Engine: Near-Sensor Neural Network Engine with SRAM-Based Compute-in-Memory Macro

Sensing devices at edge nodes are usually resource-constrained, such as limited battery capacity and physical size. As a result, there is a substantial demand for enhancing energy efficiency in these devices, which can otherwise hinder the deployment of more complex neural networks. This work proposes an energy-efficient computing engine, which is equipped with appropriate computing power to deploy medium-size neural networks for smart sensing at near-sensor edge nodes. SRAM-based Compute-in-Memory (CIM) macro and end-to-end digital controller comprise the engine. We successfully prototyped this engine using an FPGA platform and conducted a demonstration of image classification on the CIFAR-10 dataset. Furthermore, we implement the above design on a TSMC 40nm process, with post-simulation results indicating an impressive throughput of 368.64GOPS and an energy efficiency of 25.7TOPS/W at 10MHz operating frequency, given the memory capacity is 576kb.