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Lang."],"published-print":{"date-parts":[[2026,6,8]]},"abstract":"<jats:p>Coarse-Grained Reconfigurable Architectures (CGRAs) are a promising and versatile accelerator platform, offering a balance between the performance and efficiency of specialized accelerators and software programmability. However, their full potential is severely hindered by control flow in accelerated kernels, as control flow (e.g., loops, branches) is fundamentally incompatible with the parallel, data-driven CGRA fabric. Prior strategies to resolve this mismatch in CGRA kernel acceleration are either inefficient, sacrificing performance for generality, or lack generality due to the difficulty of adapting them across different execution models. Thus, a general and unified solution for efficient CGRA kernel acceleration remains elusive.<\/jats:p>\n                  <jats:p>\n                    This paper introduces NEURA, a unified and retargetable compilation framework that systematically resolves the control-dataflow mismatch in CGRAs. NEURA\u2019s core innovation is a novel, pure dataflow intermediate representation (IR) built on a predicated type system. In this IR, control contexts are embedded as a predicate within each data, making control an intrinsic property of data. This mechanism enables NEURA to systematically flatten complex control flow into a single unified dataflow graph. This unified representation decouples kernel representation from hardware, empowering NEURA to retarget diverse CGRAs with different execution models and microarchitectural features. When targeted to a high-performance\n                    <jats:bold>\n                      <jats:italic toggle=\"yes\">spatio-temporal<\/jats:italic>\n                    <\/jats:bold>\n                    CGRA, NEURA delivers a\n                    <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                      <mml:mrow>\n                        <mml:mn>2.20<\/mml:mn>\n                        <mml:mtext>\u00d7<\/mml:mtext>\n                      <\/mml:mrow>\n                    <\/mml:math>\n                    speedup on kernel benchmarks and up to\n                    <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                      <mml:mrow>\n                        <mml:mn>2.71<\/mml:mn>\n                        <mml:mtext>\u00d7<\/mml:mtext>\n                      <\/mml:mrow>\n                    <\/mml:math>\n                    geometric mean speedup on real-world applications over state-of-the-art (SOTA) high-performance baselines. It also provides a competitive solution against the SOTA low-power CGRA when retargeted to a\n                    <jats:bold>\n                      <jats:italic toggle=\"yes\">spatial-only<\/jats:italic>\n                    <\/jats:bold>\n                    CGRA.\n                    <jats:bold>NEURA is open-source and available at<\/jats:bold>\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/coredac\/neura\">\n                      <jats:bold>https:\/\/github.com\/coredac\/neura<\/jats:bold>\n                    <\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3808285","type":"journal-article","created":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T18:04:09Z","timestamp":1780941849000},"page":"953-979","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["NEURA: A Unified and Retargetable Compilation Framework for Coarse-Grained Reconfigurable Architectures"],"prefix":"10.1145","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-7623-4178","authenticated-orcid":false,"given":"Shangkun","family":"Li","sequence":"first","affiliation":[{"name":"The Hong Kong University of Science and Technology, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7986-4886","authenticated-orcid":false,"given":"Jinming","family":"Ge","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2696-6651","authenticated-orcid":false,"given":"Diyuan","family":"Tao","sequence":"additional","affiliation":[{"name":"Independent Researcher, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5104-7466","authenticated-orcid":false,"given":"Zeyu","family":"Li","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0748-3183","authenticated-orcid":false,"given":"Jiawei","family":"Liang","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3007-4890","authenticated-orcid":false,"given":"Linfeng","family":"Du","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9089-7752","authenticated-orcid":false,"given":"Jiang","family":"Xu","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7622-6714","authenticated-orcid":false,"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3727-2889","authenticated-orcid":false,"given":"Cheng","family":"Tan","sequence":"additional","affiliation":[{"name":"Google, Mountain View, USA"},{"name":"Arizona State University, Tempe, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,6,8]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/360018.360025"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/567067.567085"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3620665.3640366"},{"key":"e_1_3_1_5_2","unstructured":"\u2018arith\u2019 Dialect. 2025. \u2018arith\u2019 Dialect. https:\/\/mlir.llvm.org\/docs\/Dialects\/ArithOps\/ Accessed on Sept 15 2025."},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/12.48862"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/1024393.1024396"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","unstructured":"Yunji Chen Tao Luo Shaoli Liu Shijin Zhang Liqiang He Jia Wang Ling Li Tianshi Chen Zhiwei Xu Ninghui Sun and Olivier Temam. 2014. 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