{"ID":2870414,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.13029","arxiv_id":"2509.13029","title":"Orthrus: Dual-Loop Automated Framework for System-Technology Co-Optimization","abstract":"With the diminishing return from Moore's Law, system-technology co-optimization (STCO) has emerged as a promising approach to sustain the scaling trends in the VLSI industry. By bridging the gap between system requirements and technology innovations, STCO enables customized optimizations for application-driven system architectures. However, existing research lacks sufficient discussion on efficient STCO methodologies, particularly in addressing the information gap across design hierarchies and navigating the expansive cross-layer design space. To address these challenges, this paper presents Orthrus, a dual-loop automated framework that synergizes system-level and technology-level optimizations. At the system level, Orthrus employs a novel mechanism to prioritize the optimization of critical standard cells using system-level statistics. It also guides technology-level optimization via the normal directions of the Pareto frontier efficiently explored by Bayesian optimization. At the technology level, Orthrus leverages system-aware insights to optimize standard cell libraries. It employs a neural network-assisted enhanced differential evolution algorithm to efficiently optimize technology parameters. Experimental results on 7nm technology demonstrate that Orthrus achieves 12.5% delay reduction at iso-power and 61.4% power savings at iso-delay over the baseline approaches, establishing new Pareto frontiers in STCO.","short_abstract":"With the diminishing return from Moore's Law, system-technology co-optimization (STCO) has emerged as a promising approach to sustain the scaling trends in the VLSI industry. By bridging the gap between system requirements and technology innovations, STCO enables customized optimizations for application-driven system a...","url_abs":"https://arxiv.org/abs/2509.13029","url_pdf":"https://arxiv.org/pdf/2509.13029v1","authors":"[\"Yi Ren\",\"Baokang Peng\",\"Chenhao Xue\",\"Kairong Guo\",\"Yukun Wang\",\"Guoyao Cheng\",\"Yibo Lin\",\"Lining Zhang\",\"Guangyu Sun\"]","published":"2025-09-16T12:50:02Z","proceeding":"cs.AR","tasks":"[\"cs.AR\"]","methods":"[]","has_code":false}