Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/213076
Title: LISA: Graph Neural Network based Portable Mapping on Spatial Accelerators
Authors: Zhaoying Li
Dan Wu
D.M. DHANANJAYA WIJERATHNE 
TULIKA MITRA 
Issue Date: 2022
Publisher: IEEE
Citation: Zhaoying Li, Dan Wu, D.M. DHANANJAYA WIJERATHNE, TULIKA MITRA (2022). LISA: Graph Neural Network based Portable Mapping on Spatial Accelerators. IEEE International Symposium on High-Performance Computer Architecture. ScholarBank@NUS Repository.
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Abstract: Spatial accelerators, such as Coarse-Grained Reconfigurable Arrays (CGRA), provide a promising pathway to scale the performance and power efficiency of computing systems. These accelerators depend on effective compilers to take advantage of the parallelism offered by the underlying architecture. Currently, the compilers are handcrafted for spatial accelerators, which is challenging from time to market perspective, especially with the rapid increase of diverse accelerators. In this paper, we present a portable compilation framework, called LISA, that can be tuned automatically to generate quality mapping for varied spatial accelerators. Our key contribution is to automatically identify the impact of the dataflow graph (DFG) structure characteristics (representing an application) on the mapping for a new accelerator. Towards this end, we abstract the DFG structure in graph attributes, use Graph Neural Network (GNN) to analyze the graph attributes, and identify the mapping impact for an accelerator architecture with an all-encompassing global view. Finally, we augment a simulated annealing-based mapping approach to take into account the impact of DFG structure in guiding the placement of the dataflow graph nodes and the routing of the dependencies on the accelerator. Our experimental evaluation concretely demonstrates the substantial benefit of our approach compared to the state-of-the-art solutions.
Source Title: IEEE International Symposium on High-Performance Computer Architecture
URI: https://scholarbank.nus.edu.sg/handle/10635/213076
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections:Staff Publications
Elements
Students Publications

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
HPCA_LISA_2022.pdf2.55 MBAdobe PDF

OPEN

UnpublishedView/Download

Page view(s)

212
checked on Oct 6, 2022

Download(s)

27
checked on Oct 6, 2022

Google ScholarTM

Check


This item is licensed under a Creative Commons License Creative Commons