Quantized Neural Networks and Neuromorphic Computing for Embedded Systems

Abstract

Deep learning techniques have made great success in areas such as computer vision, speech recognition and natural language processing. Those breakthroughs made by deep learning techniques are changing every aspect of our lives. However, deep learning techniques have not realized their full potential in embedded systems such as mobiles, vehicles etc. because the high performance of deep learning techniques comes at the cost of high computation resource and energy consumption. Therefore, it is very challenging to deploy deep learning models in embedded systems because such systems have very limited computation resources and power constraints. Extensive research on deploying deep learning techniques in embedded systems has been conducted and considerable progress has been made. In this book chapter, we are going to introduce two approaches. The first approach is model compression, which is one of the very popular approaches proposed in recent years. Another approach is neuromorphic computing, which is a novel computing system that mimicks the human brain.

Encoding integers and rationals on neuromorphic computers using virtual neuron

“…Since then, it is primarily used for SNN-based machine learning applications, including computer vision 21 , natural language 22, and speech recognition 23 . These applications are mainly found in embedded systems, edge computing, and Internet of Things (IoT) devices because they have strict requirements for size, weight, and power [24][25][26] . Several on-chip and off-chip learning algorithms that leverage gradient-based and local learning rules have been suggested for training SNNs in neuromorphic applications [27][28][29][30] .…”