Recent Progress in Real‐Time Adaptable Digital Neuromorphic Hardware

Abstract

It has been three decades since neuromorphic engineering was first brought to public attention, which aimed to reverse-engineer the brain using analog, very large-scale, integrated circuits. Vigorous research in the past three decades has enriched neuromorphic systems for realizing this ambitious goal. Reverse engineering the brain essentially implies the inference and learning capabilities of a standalone neuromorphic system—particularly, the latter is referred to as embedded learning. The reconfigurability of a neuromorphic system is also pursued to make the system field-programmable. Bearing these desired attributes in mind, recent progress in digital neuromorphic hardware is overviewed, with an emphasis on real-time inference and adaptation. Real-time adaptation, that is, learning in realtime, highlights the feat of spiking neural networks with inherent rich dynamics, which allows the networks to learn from environments embodying an enormous amount of data. The realization of real-time adaptation imposes severe constraints on digital neuromorphic hardware design. Herein, the constraints and recent attempts to cope with the challenges arising from the constraints are addressed.