A Design Methodology for Post-Moore's Law Accelerators: The Case of a Photonic Neuromorphic Processor

TL;DR

This paper introduces a design methodology that integrates photonic component models into high-level neural network design tools, enabling efficient development of photonic neuromorphic processors amid the shift from traditional electronics due to the end of Moore's Law.

Contribution

It presents a novel design methodology that bridges high-level neural network tools with photonic hardware modeling, facilitating the development of specialized neuromorphic accelerators.

Findings

The methodology enables efficient exploration of photonic neuromorphic design space.

Design examples demonstrate effective navigation and system development.

The approach supports hardware-aware neural network system design.

Abstract

Over the past decade alternative technologies have gained momentum as conventional digital electronics continue to approach their limitations, due to the end of Moore's Law and Dennard Scaling. At the same time, we are facing new application challenges such as those due to the enormous increase in data. The attention, has therefore, shifted from homogeneous computing to specialized heterogeneous solutions. As an example, brain-inspired computing has re-emerged as a viable solution for many applications. Such new processors, however, have widened the abstraction gamut from device level to applications. Therefore, efficient abstractions that can provide vertical design-flow tools for such technologies became critical. Photonics in general, and neuromorphic photonics in particular, are among the promising alternatives to electronics. While the arsenal of device level toolbox for photonics, and high-level neural network platforms are rapidly expanding, there has not been much work to bridge this gap. Here, we present a design methodology to mitigate this problem by extending high-level hardware-agnostic neural network design tools with functional and performance models of photonic components. In this paper we detail this tool and methodology by using design examples and associated results. We show that adopting this approach enables designers to efficiently navigate the design space and devise hardware-aware systems with alternative technologies.