{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/data-driven-offline-optimization-for-1","title":"Data-Driven Offline Optimization For Architecting Hardware Accelerators","arxiv_id":"2110.11346","date":"2021-10-20","proceeding":"ICLR 2022 4","authors":["Aviral Kumar","Amir Yazdanbakhsh","Milad Hashemi","Kevin Swersky","Sergey Levine"],"abstract":"Industry has gradually moved towards application-specific hardware accelerators in order to attain higher efficiency. While such a paradigm shift is already starting to show promising results, designers need to spend considerable manual effort and perform a large number of time-consuming simulations to find accelerators that can accelerate multiple target applications while obeying design constraints. Moreover, such a \"simulation-driven\" approach must be re-run from scratch every time the set of target applications or design constraints change. An alternative paradigm is to use a \"data-driven\", offline approach that utilizes logged simulation data, to architect hardware accelerators, without needing any form of simulations. Such an approach not only alleviates the need to run time-consuming simulation, but also enables data reuse and applies even when set of target applications changes. In this paper, we develop such a data-driven offline optimization method for designing hardware accelerators, dubbed PRIME, that enjoys all of these properties. Our approach learns a conservative, robust estimate of the desired cost function, utilizes infeasible points, and optimizes the design against this estimate without any additional simulator queries during optimization. PRIME architects accelerators -- tailored towards both single and multiple applications -- improving performance upon state-of-the-art simulation-driven methods by about 1.54x and 1.20x, while considerably reducing the required total simulation time by 93% and 99%, respectively. In addition, PRIME also architects effective accelerators for unseen applications in a zero-shot setting, outperforming simulation-based methods by 1.26x.","url_abs":"https://arxiv.org/abs/2110.11346v3","url_pdf":"https://arxiv.org/pdf/2110.11346v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"data-driven-offline-optimization-for-1","repo_url":"https://github.com/google-research/google-research/tree/master/prime","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"jax","reach":null}],"tasks":[{"task_slug":"computer-architecture-and-systems","task_name":"Computer Architecture and Systems"}],"methods":[],"datasets_introduced":[{"slug":"prime","name":"PRIME","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2110.11346","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.11346"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/google-research/google-research/tree/master/prime","reach":null}],"summary":{"ran_draft_wrong":3},"by_repo_kind":{"official":{"samples":3,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"227845e7ae35e1ae","entry":"create_dual_approx","repo":"google-research/google-research","repo_kind":"official","path":"caql/dual_method.py","file_url":"https://github.com/google-research/google-research/blob/HEAD/caql/dual_method.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"227845e7ae35e1ae"}},{"code_sha256_prefix":"30bfbd4d2d425490","entry":"get_D","repo":"google-research/google-research","repo_kind":"official","path":"caql/dual_method.py","file_url":"https://github.com/google-research/google-research/blob/HEAD/caql/dual_method.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"30bfbd4d2d425490"}},{"code_sha256_prefix":"9386692a7f6294e9","entry":"get_I","repo":"google-research/google-research","repo_kind":"official","path":"caql/dual_method.py","file_url":"https://github.com/google-research/google-research/blob/HEAD/caql/dual_method.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"9386692a7f6294e9"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}