{"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/gemmini-an-agile-systolic-array-generator","title":"Gemmini: Enabling Systematic Deep-Learning Architecture Evaluation via Full-Stack Integration","arxiv_id":"1911.09925","date":"2019-11-22","proceeding":null,"authors":["Hasan Genc","Seah Kim","Alon Amid","Ameer Haj-Ali","Vighnesh Iyer","Pranav Prakash","Jerry Zhao","Daniel Grubb","Harrison Liew","Howard Mao","Albert Ou","Colin Schmidt","Samuel Steffl","John Wright","Ion Stoica","Jonathan Ragan-Kelley","Krste Asanovic","Borivoje Nikolic","Yakun Sophia Shao"],"abstract":"DNN accelerators are often developed and evaluated in isolation without considering the cross-stack, system-level effects in real-world environments. This makes it difficult to appreciate the impact of System-on-Chip (SoC) resource contention, OS overheads, and programming-stack inefficiencies on overall performance/energy-efficiency. To address this challenge, we present Gemmini, an open-source*, full-stack DNN accelerator generator. Gemmini generates a wide design-space of efficient ASIC accelerators from a flexible architectural template, together with flexible programming stacks and full SoCs with shared resources that capture system-level effects. Gemmini-generated accelerators have also been fabricated, delivering up to three orders-of-magnitude speedups over high-performance CPUs on various DNN benchmarks. * https://github.com/ucb-bar/gemmini","url_abs":"https://arxiv.org/abs/1911.09925v3","url_pdf":"https://arxiv.org/pdf/1911.09925v3.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":"gemmini-an-agile-systolic-array-generator","repo_url":"https://github.com/ucb-bar/gemmini","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"gemmini-an-agile-systolic-array-generator","repo_url":"https://github.com/QuarantineGemmini/chipyard","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"gemmini-an-agile-systolic-array-generator","repo_url":"https://github.com/harrisonliew/cs252_ee290_project","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"gemmini-an-agile-systolic-array-generator","repo_url":"https://github.com/ucb-bar/chipyard","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"gemmini-an-agile-systolic-array-generator","repo_url":"https://github.com/willislwang/cs152_lab3_boom","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}