Collaborative Research: Advancing Science with Accelerated Machine Learning

在下一代大科学实验中，对计算资源的需求预计将超过现有计算基础设施的能力。有鉴于此，需要对网络基础设施进行彻底反思，以应对这些发展。随着深度学习的兴起，并行处理体系结构应运而生。与深度学习算法相结合，并行处理结构，特别是现场可编程门阵列(现场可编程门阵列)，与传统的CPU相比，在计算上有很大的加速比。该项目旨在通过两个大数据物理实验：大型强子对撞机(LHC)和激光干涉仪引力波天文台(LIGO)，将基于机器学习的FPGA加速计算引入科学界。该项目将大规模推动深度学习的前沿，展示这些方法的通用性和可扩展性，以加速并使大数据时代的新物理成为可能。正如美国国家科学基金会的使命所说，这个项目通过促进科学进步来服务于国家利益。PI和他们的合作者将以他们最近的工作为基础，设计和利用最先进的神经网络模型来进行实时数据分析，从而减少总体计算延迟。这一新的计算模式旨在显著提高大型强子对撞机和LIGO的处理能力，从而增加这些设备的科学产出，并有可能实现基础性发现。在这项研究中接受指导和培训的学生将与行业合作伙伴密切互动，创造新的职业机会，并加强学术界和行业之间的协同效应。除了通过开源存储库与社区共享算法外，该团队还将继续教育社区有关科学软件的信用和引用。在这个项目中，PI将在他们最近的工作基础上开发高质量的深度学习算法，用于使用现场可编程门阵列(FGA)对时间序列和图像数据集进行实时数据分析，以加速机器学习算法的低延迟推理。该团队将开发基于机器学习的加速工具，重点是LIGO和大型强子对撞机实验中使用的现场可编程门阵列。该团队的近期目标是采用大型强子对撞机高级触发处理和LIGO引力波处理的基准例子，并在每种情况下建造演示程序。在这个基准测试中，他们的目标是设计和实现一个基于FPGA的加速器，可以执行低延迟引力波识别和大型强子对撞机事件重建。此外，PI旨在为现场可编程门阵列增加基于图形的神经网络加速器的能力。作为这些活动的一部分而开发的开源工具将很容易与LIGO、LHC和LSST共享。该项目将创建一个顾问小组，成员包括大小项目成员、中微子物理学成员、多信使天文学社区成员、行业合作伙伴、计算机科学家和计算生物学家。该项目旨在使将从这项工作中受益并能够为其作出贡献的不同社区的代表汇聚一堂。私人投资机构将组织深度学习讲习班和新兵训练营，培训学生和研究人员如何使用和促进该框架，在关键科学任务中建立一个由贡献者和开发人员组成的广泛网络。该项目是国家科学基金会利用数据革命(HDR)大创意活动的一部分。该项目是国家科学基金会利用数据革命大创意活动的一部分。这项工作由高级网络基础设施办公室共同资助。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

In the next generation of big science experiments, the demands for computing resources are expected to outstrip the capabilities of existing computing infrastructure. In light of this, a radical rethinking of the cyberinfrastructure is needed to contend with these developments. With the onset of deep learning, parallelized processing architectures have emerged as a solution. Combined with deep learning algorithms, parallelized processing architectures, in particular, Field Programmable Gate Arrays (FPGAs) have been shown to give large speedups in computing when compared with conventional CPUs. This project aims to bring machine learning based accelerated computing with FPGAs into the scientific community by targeting two big-data physics experiments: the Large Hadron Collider (LHC) and the Laser Interferometer Gravitational-wave Observatory (LIGO). This project will push the frontiers of deep learning at scale, demonstrating the versatility and scalability of these methods to accelerate and enable new physics in the big data era. This project serves the national interest, as stated by NSF's mission, by promoting the progress of science. The PIs and their collaborators will build upon their recent work to design and exploit state-of-the-art neural network models for real-time data analytics, reducing overall computing latency. This new computing paradigm aims to significantly increase the processing capability at the LHC and LIGO, leading to an increased scientific output of these devices and, potentially, foundational discoveries. The students to be mentored and trained in this research will interact closely with industry partners, creating new career opportunities, and strengthening synergies between academia and industry. In addition to sharing algorithms with the community through open source repositories, the team will continue to educate the community regarding credit and citation of scientific software.In this project, the PIs will build upon their recent work developing high quality deep learning algorithms for real-time data analytics of time-series and image datasets using Field Programmable Gate Arrays (FPGAs) to accelerate low-latency inference of machine learning algorithms. The team will develop machine learning based acceleration tools focusing on FPGAs to be used within LIGO and the LHC experiments. The team's immediate goal is to take benchmark examples of LHC high level trigger processing and LIGO gravitational wave processing and construct demonstrators in each scenario. For this benchmark, they aim to design and implement an FPGA based accelerator that can perform low latency gravitational wave identification and LHC event reconstruction. Additionally, the PIs aim to add the capability of graph based neural network accelerators for FPGAs. The open source tools to be developed as part of these activities will be readily shared with LIGO, LHC, and LSST. The project will create an advisory group, including members of large and small projects, members of the neutrino physics, multi-messenger astronomy community, industry partners, computer scientists, and computational biologists. This project aims to bring together representatives of the different communities that will benefit from and can contribute to this work. The PIs will organize deep learning workshops and boot camps to train students and researchers on how to use and contribute to the framework, creating a wide network of contributors and developers across key science missions. This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity.This project is part of the National Science Foundation's Harnessing the Data Revolution Big Idea activity. The effort is jointly funded by the Office of Advanced Cyberinfrastructure.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.