Graph Representation Learning for Microarchitecture Design Space Exploration

Design optimization of modern microprocessors is a complex task due to the exponential growth of the design space. This work presents GRL-DSE, an automatic microarchitecture search framework based on graph embeddings. GRL-DSE uses graph representation learning to build a compact and continuous embed...

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Bibliographic Details
Published in:2023 60th ACM/IEEE Design Automation Conference (DAC) pp. 1 - 6
Main Authors: Yi, Xiaoling, Lu, Jialin, Xiong, Xiankui, Xu, Dong, Shang, Li, Yang, Fan
Format: Conference Proceeding
Language:English
Published: IEEE 09.07.2023
Subjects:
Benchmark testing
Manuals
Microarchitecture
Microprocessors
Representation learning
Solid modeling
Space exploration
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Summary:Design optimization of modern microprocessors is a complex task due to the exponential growth of the design space. This work presents GRL-DSE, an automatic microarchitecture search framework based on graph embeddings. GRL-DSE uses graph representation learning to build a compact and continuous embedding space. Multi-objective Bayesian optimization using an ensemble surrogate model conducts microarchitecture design space exploration in the graph embedding space to efficiently and holistically optimize performance-power-area (PPA) objectives. Experimental studies on RISC-V BOOM show that GRLDSE outperforms previous techniques by 74.59% on Pareto front quality and outperforms manual designs in terms of PPA.
DOI:10.1109/DAC56929.2023.10247687