A Case Study on the Performance Metrics of Integrated Photonic Computing

TL;DR

This paper evaluates the performance and efficiency of different integrated photonic computing architectures, considering both photonic and electronic components, and explores a heterogenous approach for neural network inference.

Contribution

It provides a comprehensive microarchitecture-level analysis of photonic computing architectures, including peripheral electronics and in-memory processing for neural networks.

Findings

Photonic architectures vary in compute efficiency and density.

Peripheral electronics significantly impact overall system performance.

Heterogeneous photonic-electronic in-memory processing enables low latency neural inference.

Abstract

Photonic processors use optical signals for computation, leveraging the high bandwidth and low loss of optical links. While many approaches have been proposed, including in memory photonic circuits, most efforts have focused on the physical design of photonic components rather than full architectural integration with electronic peripheral circuitry. In this work, we present a microarchitecture level study that estimates the compute efficiency and density of three prominent photonic computing architectures. Our evaluation accounts for both the photonic circuits and the essential peripheral electronics required for optoelectronic and analog to digital conversions. We further demonstrate a heterogenous photonic electronic in memory processing approach for low latency neural network inference. These results provide a better understanding of the design aspects of photonic computing.