GHOST: a graph neural network accelerator using silicon photonics
| dc.contributor.author | Afifi, Salma, author | |
| dc.contributor.author | Sunny, Febin, author | |
| dc.contributor.author | Shafiee, Amin, author | |
| dc.contributor.author | Nikdast, Mahdi, author | |
| dc.contributor.author | Pasricha, Sudeep, author | |
| dc.contributor.author | ACM, publisher | |
| dc.date.accessioned | 2024-11-11T19:31:38Z | |
| dc.date.available | 2024-11-11T19:31:38Z | |
| dc.date.issued | 2023-09-09 | |
| dc.description.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 10.2 × better throughput and 3.8 × better energy efficiency when compared to GPU, TPU, CPU and multiple state-of-the-art GNN hardware accelerators. | |
| dc.format.medium | born digital | |
| dc.format.medium | articles | |
| dc.identifier.bibliographicCitation | Salma Afifi, Febin Sunny, Amin Shafiee, Mahdi Nikdast, and Sudeep Pasricha. 2023. GHOST: A Graph Neural Network Accelerator using Silicon Photonics. ACM Trans. Embedd. Comput. Syst. 22, 5s, Article 102 (September 2023), 25 pages. https://doi.org/10.1145/3609097 | |
| dc.identifier.doi | https://doi.org/10.1145/3609097 | |
| dc.identifier.uri | https://hdl.handle.net/10217/239521 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | Publications | |
| dc.relation.ispartof | ACM DL Digital Library | |
| dc.rights | © Salma Afifi, et al. | ACM 2023. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on Embedded Computing Systems, https://dx.doi.org/10.1145/3609097. | |
| dc.subject | graph neural networks | |
| dc.subject | silicon photonics | |
| dc.subject | optical computing | |
| dc.title | GHOST: a graph neural network accelerator using silicon photonics | |
| dc.type | Text |
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