Editorial: Neuro-inspired computing for next-gen AI: Computing model, architectures and learning algorithms

Abstract

Today's advances in Artificial Intelligence (AI) have been primarily driven by deep learning and have led to astounding progress in several tasks such as image classification, multiple object detection, language translation, speech recognition and even in the ability to play strategic games. However, the AI systems of today have several limitations. Specifically, the hardware infrastructure is limited to high-power and large-scale processing systems that are based on the von Neumann computing paradigm. Moreover, there is a growing demand for applications with cognitive functionality that will be able to operate in real time and in an autonomous manner in the field. The limitations of contemporary AI systems are in stark contrast to the capabilities of the brain which can learn and adapt very quickly consuming just about 20 W of power. Neuromorphic computing, inspired by neuroscience, is a promising path toward the next-generation AI systems. The research focuses on different levels of the design stack, i.e., the computing model, the architecture and the learning algorithms. The computing model is based on Spiking Neural Networks (SNNs), which possess more biologically realistic neuronal dynamics as compared to those of Artificial Neural Networks (ANNs). At the architectural level, SNNs implement in-memory computing, which is well suited for efficient SNN hardware realizations. At the algorithmic level, neuro-inspired learning paradigms are based on the insight that the brain continuously processes incoming information and is able to adapt to changing conditions. Thus, online learning, learning-to-learn, and unsupervised learning provide the main conceptual platforms for the design of low-power, accurate and reliable neuromorphic computing systems. This Research Topic provides an overview of the recent advances on computing models, architecture, and learning algorithms for neuromorphic computing. In the rest of this Editorial, we provide a brief description of the accepted papers contributing to each of these areas.