Editorial: Focus Issue on In-Memory Computing

Abstract

Neuromorphic technologies aim to use the organizing principles of the brain to build efficient and intelligent systems, making them the center-piece between the biological and current Artificial Intelligence (AI) systems. Specifically, in conventional AI systems, one of the dominant sources of power consumption is the data movement between the memory and the processor units, known as the von Neumann bottleneck. In-memory computing solves this problem by co-locating memory and processing units, drastically reducing the power as the data are processed where they reside.