GHOST: A Graph Neural Network Accelerator using Silicon Photonics

TL;DR

GHOST is a pioneering silicon-photonic hardware accelerator designed specifically for graph neural networks, achieving significant improvements in throughput and energy efficiency over traditional electronic accelerators.

Contribution

This paper introduces GHOST, the first silicon-photonic GNN accelerator, addressing the unique computational and memory challenges of GNNs with a novel optical domain implementation.

Findings

GHOST achieves at least 10.2x higher throughput than GPUs and TPUs.

GHOST provides 3.8x better energy efficiency compared to existing hardware.

It supports various GNN models like graph convolution and attention networks.

Abstract

Graph neural networks (GNNs) have emerged as a powerful approach for modelling and learning from graph-structured data. Multiple fields have since benefitted enormously from the capabilities of GNNs, such as recommendation systems, social network analysis, drug discovery, and robotics. However, accelerating and efficiently processing GNNs require a unique approach that goes beyond conventional artificial neural network accelerators, due to the substantial computational and memory requirements of GNNs. The slowdown of scaling in CMOS platforms also motivates a search for alternative implementation substrates. In this paper, we present GHOST, the first silicon-photonic hardware accelerator for GNNs. GHOST efficiently alleviates the costs associated with both vertex-centric and edge-centric operations. It implements separately the three main stages involved in running GNNs in the optical domain, allowing it to be used for the inference of various widely used GNN models and architectures, such as graph convolution networks and graph attention networks. Our simulation studies indicate that GHOST exhibits at least 10.2x better throughput and 3.8x better energy efficiency when compared to GPU, TPU, CPU and multiple state-of-the-art GNN hardware accelerators.