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PT-Map: Efficient Program Transformation Optimization for CGRA Mapping

Published: 07 November 2024 Publication History

Abstract

Coarse-Grained Reconfigurable Array (CGRA) is a parallel architecture providing high energy efficiency and spatial-temporal re-configurability. Beyond loop scheduling for throughput optimization, program transformation is also crucial in CGRA mapping to optimize overall performance and efficiency. However, existing studies on program transformation optimization face challenges in exploring the transformation space systematically and evaluating candidates efficiently, leading to sub-optimal results. To tackle these challenges, this paper introduces PT-Map, an efficient program transformation optimization framework for CGRA mapping. PT-Map defines a comprehensive transformation space and employs a CGRA-specialized top-down exploration approach. It also incorporates a bottom-up evaluation scheme using architectural parameters and a graph neural network-based predictive model. Experiments demonstrate that PT-Map achieves up to 2.95X/1.80X speedups and 59.0%/23.2% energy-delay-product (EDP) reductions over the state-of-the-art approaches MapZero and PBP, respectively.

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cover image ACM Conferences
DAC '24: Proceedings of the 61st ACM/IEEE Design Automation Conference
June 2024
2159 pages
ISBN:9798400706011
DOI:10.1145/3649329
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 07 November 2024

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DAC '24
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DAC '24: 61st ACM/IEEE Design Automation Conference
June 23 - 27, 2024
CA, San Francisco, USA

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Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

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