Architecture-Level Modeling of Photonic Deep Neural Network Accelerators

TL;DR

This paper presents a system-level modeling approach for photonic deep neural network accelerators, enabling energy-efficient design and optimization by considering full-system costs including data conversions and DRAM access.

Contribution

It introduces a novel modeling tool leveraging similarities with compute-in-memory systems to evaluate and optimize photonic neural network accelerators at the system level.

Findings

Conversion and DRAM access can consume significant energy in photonic systems.

Optimizations reducing conversions and DRAM accesses can improve energy efficiency by up to 3x.

The open-source model facilitates rapid design space exploration and co-design of photonic accelerators.

Abstract

Photonics is a promising technology to accelerate Deep Neural Networks as it can use optical interconnects to reduce data movement energy and it enables low-energy, high-throughput optical-analog computations. To realize these benefits in a full system (accelerator + DRAM), designers must ensure that the benefits of using the electrical, optical, analog, and digital domains exceed the costs of converting data between domains. Designers must also consider system-level energy costs such as data fetch from DRAM. Converting data and accessing DRAM can consume significant energy, so to evaluate and explore the photonic system space, there is a need for a tool that can model these full-system considerations. In this work, we show that similarities between Compute-in-Memory (CiM) and photonics let us use CiM system modeling tools to accurately model photonics systems. Bringing modeling tools to photonics enables evaluation of photonic research in a full-system context, rapid design space exploration, co-design, and comparison between systems. Using our open-source model, we show that cross-domain conversion and DRAM can consume a significant portion of photonic system energy. We then demonstrate optimizations that reduce conversions and DRAM accesses to improve photonic system energy efficiency by up to 3x.