Rebooting Neuromorphic Hardware Design -- A Complexity Engineering Approach

TL;DR

This paper advocates for a complexity engineering approach to neuromorphic hardware design, emphasizing the adaptation of design methodologies to leverage emerging devices like memristors for scalable, efficient AI hardware.

Contribution

It introduces a Description<->Design framework and explores complexity engineering principles to address challenges in neuromorphic hardware development with novel computing fabrics.

Findings

Complexity engineering offers advantages over traditional neuromorphic design methods.

A reservoir computing example illustrates the potential of the proposed approach.

Modifying descriptions and methodologies is crucial for integrating emerging devices.

Abstract

As the compute demands for machine learning and artificial intelligence applications continue to grow, neuromorphic hardware has been touted as a potential solution. New emerging devices like memristors, atomic switches, etc have shown tremendous potential to replace CMOS-based circuits but have been hindered by multiple challenges with respect to device variability, stochastic behavior and scalability. In this paper we will introduce a Description<->Design framework to analyze past successes in computing, understand current problems and identify solutions moving forward. Engineering systems with these emerging devices might require the modification of both the type of descriptions of learning that we will design for, and the design methodologies we employ in order to realize these new descriptions. We will explore ideas from complexity engineering and analyze the advantages and challenges they offer over traditional approaches to neuromorphic design with novel computing fabrics. A reservoir computing example is used to understand the specific changes that would accompany in moving towards a complexity engineering approach. The time is ideal for a significant reboot of our design methodologies and success will represent a radical shift in how neuromorphic hardware is designed and pave the way for a new paradigm.