mlsanityData to 14 Oct 2026

PrefixRL: Optimization of Parallel Prefix Circuits using Deep Reinforcement Learning

arXiv 2205.07000 ↗First posted 14 May 2022cs.LG cs.AI cs.ARcited by 18

Not on the map. Why?

Abstract (arXiv)

In this work, we present a reinforcement learning (RL) based approach to designing parallel prefix circuits such as adders or priority encoders that are fundamental to high-performance digital design. Unlike prior methods, our approach designs solutions tabula rasa purely through learning with synthesis in the loop. We design a grid-based state-action representation and an RL environment for constructing legal prefix circuits. Deep Convolutional RL agents trained on this environment produce prefix adder circuits that Pareto-dominate existing baselines with up to 16.0% and 30.2% lower area for the same delay in the 32b and 64b settings respectively. We observe that agents trained with open-source synthesis tools and cell library can design adder circuits that achieve lower area and delay than commercial tool adders in an industrial cell library.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Builds on 5 of its 27 references that are in the map. First posted on arXiv 14 May 2022.

Builds on

5 of 27 references.

References · 5

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  3. 2016Multi-Objective Deep Reinforcement Learningcited by 83
  4. 2020Chip Placement with Deep Reinforcement Learningcited by 83
  5. 2018Cross-layer Optimization for High Speed Adders: A Pareto Driven Machine Learning Approachcited by 7

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 8; 2024: 6; 2025: 3.

18 papers.

Sort:

By area:

  1. 2023ChipNeMo: Domain-Adapted LLMs for Chip Designcited by 174
  2. 2023Scientists' Perspectives on the Potential for Generative AI in their Fieldscited by 22
  3. 2022General Intelligence Requires Rethinking Explorationcited by 20
  4. 2024Entity-based Reinforcement Learning for Autonomous Cyber Defencecited by 11
  5. 2024Scalable and Effective Arithmetic Tree Generation for Adder and Multiplier Designscited by 9
  6. 2023Road Planning for Slums via Deep Reinforcement Learningcited by 7
  7. 2023ArchGym: An Open-Source Gymnasium for Machine Learning Assisted Architecture Designcited by 6
  8. 2024CoDreamer: Communication-Based Decentralised World Modelscited by 5
  9. 2023X-RLflow: Graph Reinforcement Learning for Neural Network Subgraphs Transformationcited by 2
  10. 2023Reinforcement Learning for Photonic Component Designcited by 2
  11. 2023OpenRL: A Unified Reinforcement Learning Frameworkcited by 2
  12. 2024RL-MUL 2.0: Multiplier Design Optimization with Parallel Deep Reinforcement Learning and Space Reductioncited by 1
6 more
  1. 2024Towards a Robust Soft Baby Robot With Rich Interaction Ability for Advanced Machine Learning Algorithmscited by 1
  2. 2023GraPhSyM: Graph Physical Synthesis Modelcited by 0
  3. 2024Scenario-based Thermal Management Parametrization Through Deep Reinforcement Learningcited by 0
  4. 2025QiMeng: Fully Automated Hardware and Software Design for Processor Chipcited by 0
  5. 2025AC-Refiner: Efficient Arithmetic Circuit Optimization Using Conditional Diffusion Modelscited by 0
  6. 2025PrefixAgent: An LLM-Powered Design Framework for Efficient Prefix Adder Optimizationcited by 0