A recipe for creating ideal hybrid memristive-CMOS neuromorphic computing systems

TL;DR

This paper presents a comprehensive guide for designing hybrid memristive-CMOS neuromorphic systems, emphasizing device specifications, design strategies, and applications in ultra-low power brain-inspired computing for IoT and edge devices.

Contribution

It introduces a novel recipe for creating efficient neuromorphic systems by integrating memristive devices with CMOS technology, inspired by mammalian brain principles.

Findings

Memristive devices suitable for always-on learning and low power consumption.

Design strategies inspired by brain functions for neuromorphic systems.

Potential applications in IoT and edge computing for sensory processing.

Abstract

The development of memristive device technologies has reached a level of maturity to enable the design of complex and large-scale hybrid memristive-CMOS neural processing systems. These systems offer promising solutions for implementing novel in-memory computing architectures for machine learning and data analysis problems. We argue that they are also ideal building blocks for the integration in neuromorphic electronic circuits suitable for ultra-low power brain-inspired sensory processing systems, therefore leading to the innovative solutions for always-on edge-computing and Internet-of-Things (IoT) applications. Here we present a recipe for creating such systems based on design strategies and computing principles inspired by those used in mammalian brains. We enumerate the specifications and properties of memristive devices required to support always-on learning in neuromorphic computing systems and to minimize their power consumption. Finally, we discuss in what cases such neuromorphic systems can complement conventional processing ones and highlight the importance of exploiting the physics of both the memristive devices and of the CMOS circuits interfaced to them.