Neuromorphic Near-Sensor Computing: From Event-Based Sensing to Edge Learning

Abstract

Neuromorphic near-sensor computing has recently emerged as a low-power and low-memory paradigm for the design of artificial intelligence (AI)-enabled IoT devices working at the extreme edge. Compared to conventional sensing and learning techniques, neuromorphic sampling, and processing reduces data bandwidth requirements, induces large savings on power and area consumption, and enables online learning and adaptation. In this article, we discuss recent studies made in the design of event-based sampling and learning circuits. We show that our event-based sampling methods outperform conventional techniques in terms of power consumption. We also show that our spiking neural network (SNN), learning through spike-timing-dependent plasticity (STDP), outperforms the state-of-the-art SNN-STDP systems in terms of inference accuracy while being orders of magnitude more power efficient than conventional deep-learning systems. We hope that the opportunities discussed in this summary article will inspire future research.