Deploying a Convolutional Neural Network on Edge MCU and Neuromorphic Hardware Platforms

Abstract

The rapid development of embedded technologies in recent decades has led to the advent of dedicated inference platforms for deep learning. However, unlike development libraries for the algorithms, hardware deployment is highly fragmented in both technology, tools, and usability. Moreover, emerging paradigms such as spiking neural networks do not use the same prediction process, making the comparison between platforms difficult. In this paper, we deploy a convolutional neural network model on different platforms comprising microcontrollers with and without deep learning accelerators and an event-based accelerator and compare their performance. We also report the perceived effort of deployment for each platform.