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

TLDR

Memristive device technology has matured enough to enable complex hybrid memristive‑CMOS neural processing systems, offering promising in‑memory computing architectures for machine learning and data analysis, and serving as ideal low‑power building blocks for neuromorphic circuits in always‑on edge‑computing and IoT applications. The paper presents a recipe for building hybrid memristive‑CMOS neuromorphic systems using brain‑inspired design strategies and computing principles. The authors enumerate the device specifications and properties needed for low‑power, always‑on learning, and discuss how exploiting the physics of memristive devices and CMOS circuits can complement conventional processors.

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.