# DAPO: Design Structure-Aware Pass Ordering in High-Level Synthesis with Graph Contrastive and Reinforcement Learning

arXiv: https://arxiv.org/abs/2512.11342 · HTML: https://mlsanity.com/paper/2512.11342/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 12 Dec 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 12 of 12)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Principal Neighbourhood Aggregation for Graph Nets](https://mlsanity.com/paper/2004.05718/) (2020)
- [IronMan: GNN-assisted Design Space Exploration in High-Level Synthesis via Reinforcement Learning](https://mlsanity.com/paper/2102.08138/) (2021)
- [AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement Learning](https://mlsanity.com/paper/2003.00671/) (2020)
- [RLlib Flow: Distributed Reinforcement Learning is a Dataflow Problem](https://mlsanity.com/paper/2011.12719/) (2020)
- [Learning Compiler Pass Orders using Coreset and Normalized Value Prediction](https://mlsanity.com/paper/2301.05104/) (2023)
- [Democratizing Domain-Specific Computing](https://mlsanity.com/paper/2209.02951/) (2022)
- [Hierarchical Source-to-Post-Route QoR Prediction in High-Level Synthesis with GNNs](https://mlsanity.com/paper/2401.08696/) (2024)
- [POSET-RL: Phase ordering for Optimizing Size and Execution Time using Reinforcement Learning](https://mlsanity.com/paper/2208.04238/) (2022)

## Abstract (arXiv)

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 classic HLS designs demonstrate that our end-to-end flow delivers a 2.36 speedup over Vitis HLS on average.