DAPO: Design Structure-Aware Pass Ordering for HLS via Contrastive and Reinforcement Learning
Abstract
High-Level Synthesis (HLS) tools are widely adopted in FPGA-based domain-specific accelerator design. However, existing tools rely on fixed optimization strategies inherited from software compilations, limiting their effectiveness. Tailoring optimization strategies to specific designs requires deep semantic understanding, accurate hardware metric estimation, and advanced search algorithms - capabilities that current approaches lack.We propose DAPO, a design structure-aware pass ordering framework that extracts program semantics from control and data flow graphs, employs contrastive learning to generate rich embeddings, and leverages an analytical model for accurate hardware metric estimation. These components jointly guide a reinforcement learning agent to discover design-specific optimization strategies. Evaluations on standard HLS benchmarks demonstrate that our end-to-end flow delivers 1.67× speedup on pragma-free designs and a 2.36× speedup on designs with pragmas over Vitis HLS with comparable resource usage.
Read the paper
Related studies
- Meta-analysis and the science of research synthesis
- Rapid and sustained symptom reduction following psilocybin treatment for anxiety and depression in patients with life-threatening cancer: a randomized controlled trial
- Cluster Randomized Controlled Trial
- Convalescent plasma in patients admitted to hospital with COVID-19 (RECOVERY): a randomised controlled, open-label, platform trial
- Disease Free Survival and Local Recurrence for Laparoscopic Resection Compared to Open Resection of Stage II-III Rectal Cancer: Follow up Results of the ACOSOG Z6051 Randomized Controlled Trial
- The effects of a course of intranasal oxytocin on social behaviors in youth diagnosed with autism spectrum disorders: a randomized controlled trial
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.