Enabling deep learning at the IoT edge

Abstract

Deep learning algorithms have demonstrated super-human capabilities in many cognitive tasks, such as image classification and speech recognition. As a result, there is an increasing interest in deploying neural networks (NNs) on low-power processors found in always-on systems, such as those based on Arm Cortex-M microcontrollers. In this paper, we discuss the challenges of deploying neural networks on microcontrollers with limited memory, compute resources and power budgets. We introduce CMSIS-NN, a library of optimized software kernels to enable deployment of NNs on Cortex-M cores. We also present techniques for NN algorithm exploration to develop light-weight models suitable for resource constrained systems, using keyword spotting as an example.