{"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/redactor-efpga-redaction-for-dnn-accelerator","title":"REDACTOR: eFPGA Redaction for DNN Accelerator Security","arxiv_id":"2501.18740","date":"2025-01-30","proceeding":null,"authors":["Yazan Baddour","Ava Hedayatipour","Amin Rezaei"],"abstract":"With the ever-increasing integration of artificial intelligence into daily life and the growing importance of well-trained models, the security of hardware accelerators supporting Deep Neural Networks (DNNs) has become paramount. As a promising solution to prevent hardware intellectual property theft, eFPGA redaction has emerged. This technique selectively conceals critical components of the design, allowing authorized users to restore functionality post-fabrication by inserting the correct bitstream. In this paper, we explore the redaction of DNN accelerators using eFPGAs, from specification to physical design implementation. Specifically, we investigate the selection of critical DNN modules for redaction using both regular and fracturable look-up tables. We perform synthesis, timing verification, and place & route on redacted DNN accelerators. Furthermore, we evaluate the overhead of incorporating eFPGAs into DNN accelerators in terms of power, area, and delay, finding it reasonable given the security benefits.","url_abs":"https://arxiv.org/abs/2501.18740v1","url_pdf":"https://arxiv.org/pdf/2501.18740v1.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":"redactor-efpga-redaction-for-dnn-accelerator","repo_url":"https://github.com/cars-lab-repo/redactor","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}