CAREER: OneSense: One-Rule-for-All Combinatorial Boolean Synthesis via Reinforcement Learning

职业：OneSense：通过强化学习进行一刀切的组合布尔综合

基本信息

批准号：
2047176
负责人：
Cunxi Yu
金额：
$ 47.85万
依托单位：
University of Utah
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-01 至 2024-02-29
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2047176&HistoricalAwards=false
关键词：
CAREER OneSense Rule Combinatorial Boolean

项目摘要

Combinatorial optimization problems over graphs arising from numerous application domains, such as planning, scheduling, and electronic design automation (EDA), are NP-hard, and have recently attracted considerable interest from the theory, algorithm design, and machine learning communities. For example, many of the EDA problems such as Boolean optimization are combinatorial optimization problems, which are unlikely to be solved by polynomial-time algorithms. In practice, those problems can be solved using scalable optimization algorithms with approximations and domain-specific heuristics, which are mostly developed by extensive hand-engineering efforts with strong domain knowledge. However, recent progress in developing such algorithms and associated heuristics is slowing down significantly due to the high barrier of technical knowledge, time-consuming hand-engineering, and several misleading designing strategies. This project aims to employ reinforcement learning and neural networks to enable self-learning high-performance algorithms and heuristics over graphs, which can outperform existing hand-crafted approaches without human supervision and domain knowledge. This will can be generalized to autonomously learn and discover novel graph-based combinatorial optimization heuristics at a wide range of application domains without any human guidance. This project will produce open-source software and conference tutorials to facilitate technology transfers and fruitful industry-academia interactions in a multidisciplinary community.This project develops the OneSense system, a graph learning driven reinforcement learning framework for exploring self-learning novel algorithms and heuristics over graphs, with special focuses on graph-based large-scale Boolean optimization problems. The core of the project includes novel reinforcement learning formulations and neural architecture with domain-specific online graph sampling techniques to enable self-learning high-performance graph optimization heuristics. The reinforcement agent with the various reward formulations and novel training methodologies and algorithms will enable effectively learning novel combinatorial optimization heuristics with a wide range of performance customization. OneSense system will be integrated with an open-source end-to-end EDA design space exploration system, which will allow productive exploration and deployment of self-learned optimization heuristics over graphs in Boolean optimization. Moreover, the OneSense reinforcement learning framework will be released to allow exploring self-learned graph optimization algorithms in other research domains and be used as an educational platform.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NP-HARD是由众多应用领域（例如计划，调度和电子设计自动化（EDA））引起的图表的组合优化问题，最近引起了理论，算法设计和机器学习社区的极大兴趣。例如，许多EDA问题（例如布尔优化）是组合优化问题，这些问题不太可能通过多项式时间算法来解决。实际上，可以使用具有近似值和特定领域的启发式方法的可扩展优化算法来解决这些问题，这些算法主要是通过具有强大领域知识的广泛的手工设计工作来解决的。但是，由于技术知识的高障碍，耗时的手工设计以及几种误导性的设计策略，开发此类算法和相关启发式方法的最新进展正在大大减慢。该项目旨在采用强化学习和神经网络，以实现对图表的自我学习高性能算法和启发式方法，而无需人类的监督和领域知识，它们可以超越现有的手工制作方法。可以将其推广到自主学习和发现新型基于图的组合优化启发式方法，该启发式启发式方法在广泛的应用领域，而无需任何人类指导。该项目将生产开源软件和会议教程，以促进技术转移和富有成果的行业 - 阿卡迪血症在跨学科社区中进行。该项目开发了Onesense System，这是一个图形学习驱动的增强增强型学习框架，用于探索自我学习的新型算法和与图形的启示术，并在图形上进行了特殊的焦点，并针对图形的大型Bolelean Boolean Boolean Boolean Boarlean Boarlean Boartivess Interiviates。该项目的核心包括新颖的增强学习公式和具有特定领域的在线图抽样技术的神经体系结构，以实现自我学习的高性能图优化启发式启发式。具有各种奖励配方和新型培训方法和算法的增强剂将有效地学习新颖的组合优化启发式方法，并具有广泛的性能自定义。 Onesense系统将与开源端到端EDA设计空间探索系统集成，这将允许在布尔优化中的图形上进行自我学习的优化启发式启发式启发式。此外，将发布OneSense增强学习框架，以允许在其他研究领域探索自我学习的图形优化算法，并用作教育平台。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的审查标准来通过评估来通过评估来支持的。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Physics-informed recurrent neural network for time dynamics in optical resonances

DOI：
10.1038/s43588-022-00215-2
发表时间：
2022-03
期刊：
Nature Computational Science
影响因子：
0
作者：
Yingheng Tang;Jichao Fan;Xinwei Li;Jianzhu Ma;M. Qi;Cunxi Yu;Weilu Gao
通讯作者：
Yingheng Tang;Jichao Fan;Xinwei Li;Jianzhu Ma;M. Qi;Cunxi Yu;Weilu Gao

FlowTune: End-to-End Automatic Logic Optimization Exploration via Domain-Specific Multiarmed Bandit

FlowTune：通过特定领域的 Multiarmed Bandit 进行端到端自动逻辑优化探索

DOI：
10.1109/tcad.2022.3213611
发表时间：
2023
期刊：
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
影响因子：
2.9
作者：
Neto, Walter Lau;Li, Yingjie;Gaillardon, Pierre-Emmanuel;Yu, Cunxi
通讯作者：
Yu, Cunxi

Device‐System End‐to‐End Design of Photonic Neuromorphic Processor Using Reinforcement Learning

使用强化学习的光子神经形态处理器的设备-系统端-端设计

DOI：
10.1002/lpor.202200381
发表时间：
2022
期刊：
Laser & Photonics Reviews
影响因子：
11
作者：
Tang, Yingheng;Zamani, Princess Tara;Chen, Ruiyang;Ma, Jianzhu;Qi, Minghao;Yu, Cunxi;Gao, Weilu
通讯作者：
Gao, Weilu

Physics‐Aware Machine Learning and Adversarial Attack in Complex‐Valued Reconfigurable Diffractive All‐Optical Neural Network

物理 - 复杂的感知机器学习和对抗性攻击 - 有价值的可重构衍射全 - 光神经网络

DOI：
10.1002/lpor.202200348
发表时间：
2022
期刊：
Laser & Photonics Reviews
影响因子：
11
作者：
Chen, Ruiyang;Li, Yingjie;Lou, Minhan;Fan, Jichao;Tang, Yingheng;Sensale‐Rodriguez, Berardi;Yu, Cunxi;Gao, Weilu
通讯作者：
Gao, Weilu

Physics-aware Differentiable Discrete Codesign for Diffractive Optical Neural Networks