A system design perspective on neuromorphic computer processors

Abstract

Neuromorphic computing has become an attractive candidate for emerging computing platforms. It requires an architectural perspective, meaning the topology or hyperparameters of a neural network is key to realizing sound accuracy and performance in neural networks. However, these network architectures must be executed on some form of computer processor. For machine learning, this is often done with conventional computer processing units, graphics processor units, or some combination thereof. A neuromorphic computer processor or neuroprocessor, in the context of this paper, is a hardware system that has been designed and optimized for executing neural networks of one flavor or another. Here, we review the history of neuromorphic computing and consider various spiking neuroprocessor designs that have emerged over the years. The aim of this paper is to identify emerging trends and techniques in the design of such brain-inspired neuroprocessor computer systems.