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Agile Hardware Project

Concept

A Stanford-based research project whose primary goal is to facilitate rapid design space exploration for coarse-grained reconfigurable arrays (CGRAs). The challenges encountered while building compilers for its rapidly evolving target architecture motivated the development of automated methods for synthesizing instruction selection rewrite rules from RTL.

First seen 6/8/2026
Last seen 6/8/2026
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Overview

The Agile Hardware Project is a research effort based at Stanford University focused on enabling rapid architectural design space exploration. According to the FMCAD 2022 paper Synthesizing Instruction Selection Rewrite Rules from RTL using SMT, the project's primary goal is to facilitate rapid design space exploration for a coarse-grained reconfigurable array (CGRA).

Motivation and Compiler Challenges

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RELATIONSHIPS

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The paper arose in the context of the Agile Hardware Project.

CITATIONS

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5 citations — click to expand
[1] The Agile Hardware Project's primary goal is to facilitate rapid design space exploration for a coarse-grained reconfigurable array (CGRA). Synthesizing Instruction Selection Rewrite Rules from RTL using SMT
[2] In the Agile Hardware Project, manually maintaining rewrite rules for a rapidly changing architecture was a constant pain point, which motivated the development of an automated synthesis method. Synthesizing Instruction Selection Rewrite Rules from RTL using SMT
[3] The automated rewrite-rule synthesis method developed in the context of the Agile Hardware Project has made possible the efficient and algorithmic exploration of large design spaces, with rewrite rule generation performed without a human in the loop. Synthesizing Instruction Selection Rewrite Rules from RTL using SMT
[4] The Agile Hardware Project is associated with Stanford University; the FMCAD 2022 paper describing work that arose in the project lists authors from Stanford. Synthesizing Instruction Selection Rewrite Rules from RTL using SMT
[5] The Agile Hardware Project's compiler-construction challenges sit within a broader research context responding to the end of Moore's law and Dennard scaling, in which future performance gains are expected from domain-specific architectures and accelerators. Synthesizing Instruction Selection Rewrite Rules from RTL using SMT