IMA-GNN: In-Memory Acceleration of Centralized and Decentralized Graph Neural Networks at the Edge
Document Type
Conference Proceeding
Publication Date
6-5-2023
Abstract
In this paper, we propose IMA-GNN as an In-Memory Accelerator for centralized and decentralized Graph Neural Network inference, explore its potential in both settings and provide a guideline for the community targeting flexible and efficient edge computation. Leveraging IMA-GNN, we first model the computation and communication latencies of edge devices. We then present practical case studies on GNN-based taxi demand and supply prediction and also adopt four large graph datasets to quantitatively compare and analyze centralized and decentralized settings. Our cross-layer simulation results demonstrate that on average, IMA-GNN in the centralized setting can obtain ∼790x communication speed-up compared to the decentralized GNN setting. However, the decentralized setting performs computation ∼1400x faster while reducing the power consumption per device. This further underlines the need for a hybrid semi-decentralized GNN approach.
Identifier
85163161678 (Scopus)
ISBN
[9798400701252]
Publication Title
Proceedings of the ACM Great Lakes Symposium on VLSI Glsvlsi
External Full Text Location
https://doi.org/10.1145/3583781.3590248
First Page
3
Last Page
8
Grant
1852375
Fund Ref
National Science Foundation
Recommended Citation
Morsali, Mehrdad; Nazzal, Mahmoud; Khreishah, Abdallah; and Angizi, Shaahin, "IMA-GNN: In-Memory Acceleration of Centralized and Decentralized Graph Neural Networks at the Edge" (2023). Faculty Publications. 1668.
https://digitalcommons.njit.edu/fac_pubs/1668