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SHF: Small: Boosting Reasoning in Boolean Networks with Attributed Graph Learning

SHF：小：通过属性图学习增强布尔网络的推理

基本信息

批准号：
2008144
负责人：
Cunxi Yu
金额：
$ 38.17万
依托单位：
University of Utah
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2024-02-29
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008144&HistoricalAwards=false
关键词：
SHF Small Boosting Reasoning Boolean

项目摘要

Boolean networks, mostly represented as graphs, have emerged as an effective logical representation to model not only the computational processes but also several phenomena from science and engineering, such as genetic analysis, electronic design automation, formal verification, etc. However, Boolean networks used in modern science and engineering applications can be extremely large with complex structures, which makes them less practical for real-world applications. For example, Boolean networks for optimizing logic circuits can have billions of vertices and cannot be effectively handled using traditional algorithms. Recent years have seen a widespread application of machine-learning (ML) techniques to various problems over graphs, namely graph learning, which has been successfully applied to accelerate applications by exploiting graph features found in social-network prediction and drug analysis. This project aims to develop a systematic framework that leverages graph learning to reason about Boolean networks, including dataset design, learning-algorithm development, training models, system integration, and evaluation over various application domains. The framework will be implemented in an extensible platform that can be used for a variety of applications in science and engineering. This project will create unique education and outreach opportunities for both academic and industrial participants, which involve mentoring of graduate and undergraduate students, innovation in teaching with investigator’s new courses in electronic design and deep learning, and attracting and preparing high-quality researchers with diverse backgrounds.The team of researchers will develop a set of novel algorithms in graph fusion, graph coarsening and refinement, and graph neural networks, to achieve high-quality and scalable embeddings for reasoning about functional, high-level abstractions of billion-node Boolean networks. The methods in this project will sit between the classical symbolic techniques in formal methods and ML in order to benefit both research communities in many domains, such as verification and synthesis, bioinformatics, artificial intelligence, and security. Specifically, the investigator plans to leverage and advance ML in symbolic-reasoning tasks, such that it can perform truly scalable Boolean reasoning analogously to traditional symbolic-reasoning approaches. The developments of this project will focus on novel algorithms in graph fusion and neural network architectures, domain-specific compression algorithms, end-to-end system integration, and large-scale system-level parallelism. In addition, the framework will be evaluated in algorithmic design-space exploration, targeting Boolean satisfiability solving and Boolean optimization.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.

布尔网络，主要表示为图，已经成为一种有效的逻辑表示，不仅可以对计算过程进行建模，还可以对科学和工程中的几种现象进行建模，例如遗传分析，电子设计自动化，形式验证等。这使得它们对于现实世界的应用不太实用。例如，用于优化逻辑电路的布尔网络可以具有数十亿个顶点，并且不能使用传统算法有效地处理。近年来，机器学习（ML）技术广泛应用于图上的各种问题，即图学习，它已成功地应用于通过利用社交网络预测和药物分析中发现的图特征来加速应用。该项目旨在开发一个系统框架，利用图学习来推理布尔网络，包括数据集设计，学习算法开发，训练模型，系统集成和各种应用领域的评估。该框架将在一个可扩展的平台中实现，该平台可用于科学和工程领域的各种应用。该项目将为学术和工业参与者创造独特的教育和推广机会，其中包括指导研究生和本科生，创新电子设计和深度学习的研究员新课程的教学，以及吸引和培养具有不同背景的高素质研究人员。研究人员团队将开发一套新颖的算法，用于图融合，图粗化和细化，和图神经网络，以实现高质量和可扩展的嵌入，用于对十亿节点布尔网络的功能，高级抽象进行推理。该项目中的方法将介于形式方法和ML中的经典符号技术之间，以使许多领域的研究社区受益，例如验证和合成，生物信息学，人工智能和安全。具体来说，研究人员计划在符号推理任务中利用和推进ML，以便它可以执行类似于传统符号推理方法的真正可扩展的布尔推理。该项目的发展将集中在图融合和神经网络架构中的新算法，特定领域的压缩算法，端到端系统集成和大规模系统级并行。此外，该框架将在算法设计-空间探索方面进行评估，目标是布尔可满足性求解和布尔优化。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（10）

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

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

IMpress: Large Integer Multiplication Expression Rewriting for FPGA HLS

DOI：
10.1109/fccm53951.2022.9786123
发表时间：
2022-05
期刊：
2022 IEEE 30th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)
影响因子：
0
作者：
Ecenur Ustun;Ismail San;Jiaqi Yin;Cunxi Yu;Zhiru Zhang
通讯作者：
Ecenur Ustun;Ismail San;Jiaqi Yin;Cunxi Yu;Zhiru Zhang

Exact Memory- and Communication-aware Scheduling of DNNs on Pipelined Edge TPUs