{"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/fpgas-as-a-service-toolkit-faast","title":"FPGAs-as-a-Service Toolkit (FaaST)","arxiv_id":"2010.08556","date":"2020-10-16","proceeding":null,"authors":["Dylan Sheldon Rankin","Jeffrey Krupa","Philip Harris","Maria Acosta Flechas","Burt Holzman","Thomas Klijnsma","Kevin Pedro","Nhan Tran","Scott Hauck","Shih-Chieh Hsu","Matthew Trahms","Kelvin Lin","Yu Lou","Ta-Wei Ho","Javier Duarte","Mia Liu"],"abstract":"Computing needs for high energy physics are already intensive and are expected to increase drastically in the coming years. In this context, heterogeneous computing, specifically as-a-service computing, has the potential for significant gains over traditional computing models. Although previous studies and packages in the field of heterogeneous computing have focused on GPUs as accelerators, FPGAs are an extremely promising option as well. A series of workflows are developed to establish the performance capabilities of FPGAs as a service. Multiple different devices and a range of algorithms for use in high energy physics are studied. For a small, dense network, the throughput can be improved by an order of magnitude with respect to GPUs as a service. For large convolutional networks, the throughput is found to be comparable to GPUs as a service. This work represents the first open-source FPGAs-as-a-service toolkit.","url_abs":"https://arxiv.org/abs/2010.08556v1","url_pdf":"https://arxiv.org/pdf/2010.08556v1.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"fpgas-as-a-service-toolkit-faast","repo_url":"https://github.com/hls-fpga-machine-learning/FaaST","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"fpgas-as-a-service-toolkit-faast","repo_url":"https://github.com/LouYu2015/ml-suite/tree/master/examples/gRPC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2010.08556","atlas_url":"https://app.syntology.ai/?focus=2010.08556","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}