{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T09:49:38Z","timestamp":1766137778562,"version":"3.41.0"},"reference-count":24,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2021,9,13]],"date-time":"2021-09-13T00:00:00Z","timestamp":1631491200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nd\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Reconfigurable Technol. Syst."],"published-print":{"date-parts":[[2021,12,31]]},"abstract":"<jats:p>Reconfigurable architectures are quickly gaining in popularity due to their flexibility and ability to provide high energy efficiency. However, reconfigurable systems allow for a huge design space. Iterative design space exploration (DSE) is often required to achieve good Pareto points with respect to some combination of performance, area, and\/or energy. DSE tools depend on information about hardware characteristics in these aspects. These characteristics can be obtained from hardware synthesis and net-list simulation, but this is very time-consuming. Therefore, architecture models are common. This work introduces CGRA-EAM (Coarse-Grained Reconfigurable Architecture - Energy &amp; Area Model), a model for energy and area estimation framework for coarse-grained reconfigurable architectures. The model is evaluated for the Blocks CGRA. The results demonstrate that the mean absolute percentage error is 15.5% and 2.1% for energy and area, respectively, while the model achieves a speedup of close to three orders of magnitude compared to synthesis.<\/jats:p>","DOI":"10.1145\/3468874","type":"journal-article","created":{"date-parts":[[2021,9,14]],"date-time":"2021-09-14T00:51:51Z","timestamp":1631580711000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["CGRA-EAM\u2014Rapid Energy and Area Estimation for Coarse-grained Reconfigurable Architectures"],"prefix":"10.1145","volume":"14","author":[{"given":"Mark","family":"Wijtvliet","sequence":"first","affiliation":[{"name":"Dresden University of Technology, Dresden, Sachsen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Henk","family":"Corporaal","sequence":"additional","affiliation":[{"name":"Eindhoven University of Technology, Eindhoven, AZ, Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Akash","family":"Kumar","sequence":"additional","affiliation":[{"name":"Dresden University of Technology, Dresden, Sachsen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,9,13]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ASPDAC.2007.358040"},{"key":"e_1_2_1_2_1","volume-title":"IEEE 28th International Conference on Application-specific Systems, Architectures and Processors (ASAP'17)","author":"Chin S. A.","year":"2017","unstructured":"S. A. Chin , N. Sakamoto , A. Rui , J. Zhao , J. H. Kim , Y. Hara-Azumi , and J. Anderson . 2017. CGRA-ME: A unified framework for CGRA modelling and exploration . In IEEE 28th International Conference on Application-specific Systems, Architectures and Processors (ASAP'17) . IEEE, New York, NY, 184\u2013189. DOI: https:\/\/doi.org\/10.1109\/ASAP. 2017 .7995277 10.1109\/ASAP.2017.7995277 S. A. Chin, N. Sakamoto, A. Rui, J. Zhao, J. H. Kim, Y. Hara-Azumi, and J. Anderson. 2017. CGRA-ME: A unified framework for CGRA modelling and exploration. In IEEE 28th International Conference on Application-specific Systems, Architectures and Processors (ASAP'17). IEEE, New York, NY, 184\u2013189. DOI: https:\/\/doi.org\/10.1109\/ASAP.2017.7995277"},{"volume-title":"IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'08)","author":"Deng Lanping","key":"e_1_2_1_3_1","unstructured":"Lanping Deng , K. Sobti , and C. Chakrabarti . 2008. Accurate models for estimating area and power of FPGA implementations . In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'08) . IEEE, New York, NY. Lanping Deng, K. Sobti, and C. Chakrabarti. 2008. Accurate models for estimating area and power of FPGA implementations. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'08). IEEE, New York, NY."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.5555\/2561828.2561918"},{"key":"e_1_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Tobias Grosser Armin Groesslinger and Christian Lengauer. 2012. Polly\u2014Performing Polyhedral Optimizations on a Low-level Intermediate Representation. 1250010 pages.  Tobias Grosser Armin Groesslinger and Christian Lengauer. 2012. 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