Neuromorphic Computing for Machine Learning Acceleration Based on Spiking Neural Network.

Recent breakthroughs in deep learning have spurred interest in novel computing architectures that can accelerate machine learning algorithms. Neuromorphic computing utilizing fully parallel operation enabled by an array of resistive elements is being actively explored to implement power- and area-efficient machine learning accelerators. To successfully develop a neuromorphic processor, it is crucial to co-optimize the device, circuit, architecture, and algorithm. In this regard, this article introduces a fully integrated neuromorphic processor using phase change memory as a synaptic device that implements a fully asynchronous and parallel operation of a spiking neural network running a restricted Boltzmann machine as a learning algorithm. [ABSTRACT FROM AUTHOR]