mlsanityData to 14 Oct 2026

DevFormer: A Symmetric Transformer for Context-Aware Device Placement

arXiv 2205.13225 ↗First posted 26 May 2022cs.LGcited by 11

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Abstract (arXiv)

In this paper, we present DevFormer, a novel transformer-based architecture for addressing the complex and computationally demanding problem of hardware design optimization. Despite the demonstrated efficacy of transformers in domains including natural language processing and computer vision, their use in hardware design has been limited by the scarcity of offline data. Our approach addresses this limitation by introducing strong inductive biases such as relative positional embeddings and action-permutation symmetricity that effectively capture the hardware context and enable efficient design optimization with limited offline data. We apply DevFoemer to the problem of decoupling capacitor placement and show that it outperforms state-of-the-art methods in both simulated and real hardware, leading to improved performances while reducing the number of components by more than 30%. Finally, we show that our approach achieves promising results in other offline contextual learning-based combinatorial optimization tasks.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 15 of its 40 references that are in the map. First posted on arXiv 26 May 2022.

Builds on

15 of 40 references.

References · 15

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  4. 2021Decision Transformer: Reinforcement Learning via Sequence Modelingcited by 1,546
  5. 2015Pointer Networkscited by 895
  6. 2018Attention, Learn to Solve Routing Problems!cited by 656
  7. 2016Neural Combinatorial Optimization with Reinforcement Learningcited by 608
  8. 2021Flow Network based Generative Models for Non-Iterative Diverse Candidate Generationcited by 392
  9. 2020POMO: Policy Optimization with Multiple Optima for Reinforcement Learningcited by 348
  10. 2015Contextual Markov Decision Processescited by 220
  11. 2019DRiLLS: Deep Reinforcement Learning for Logic Synthesiscited by 47
  12. 2021On Joint Learning for Solving Placement and Routing in Chip Designcited by 44
3 more
  1. 2019A Deep Reinforcement Learning Approach for Global Routingcited by 20
  2. 2022Transformer Network-based Reinforcement Learning Method for Power Distribution Network (PDN) Optimization of High Bandwidth Memory (HBM)cited by 5
  3. 2020Track-Assignment Detailed Routing Using Attention-based Policy Model With Supervisioncited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 6; 2025: 2; 2026: 1.

11 papers.

  1. 2024ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolutioncited by 276
  2. 2023RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmarkcited by 71
  3. 2024Ant Colony Sampling with GFlowNets for Combinatorial Optimizationcited by 53
  4. 2023Symmetric Replay Training: Enhancing Sample Efficiency in Deep Reinforcement Learning for Combinatorial Optimizationcited by 7
  5. 2024TrajDeleter: Enabling Trajectory Forgetting in Offline Reinforcement Learning Agentscited by 7
  6. 2024PARCO: Parallel AutoRegressive Models for Multi-Agent Combinatorial Optimizationcited by 7
  7. 2024RoutePlacer: An End-to-End Routability-Aware Placer with Graph Neural Networkcited by 1
  8. 2024Attention-Based Deep Reinforcement Learning for Qubit Allocation in Modular Quantum Architecturescited by 1
  9. 2025TransPlace: Transferable Circuit Global Placement via Graph Neural Networkcited by 0
  10. 2025ReflectivePrompt: Reflective evolution in autoprompting algorithmscited by 0
  11. 2026PhysEDA: Physics-Aware Learning Framework for Efficient EDA With Manhattan Distance Decaycited by 0