MRI: Development of Heterogeneous Edge Computing Platform for Real-Time Scientific Machine Learning

MRI：开发用于实时科学机器学习的异构边缘计算平台

• 批准号: 2215789
• 负责人: Lifang He
• 金额: $ 99.96万
• 依托单位: Lehigh University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2215789&HistoricalAwards=false
• 关键词: MRI; Development; Heterogeneous; Edge; Computing

This project aims to develop a Heterogeneous Edge Computing platform for real-time Scientific Machine Learning (HEC-SML) at the extreme edge. Such a platform will allow for real-time analysis and control of optical, scanning probe, and transmission electron microscopy. Putting computation at the edge –- as close to the data source as possible -– circumvents latency and bandwidth challenges when sending data to high-performance computing facilities and will enable real-time data analysis to conduct scientific experiments with creative inquiry. The development of HEC-SML will lead to the convergence of microscopy, machine learning (ML), and heterogeneous computing concepts. In microscopy, advanced control systems will enable new imaging modalities; methods for personalized medicine; and discovery and understanding of functional and quantum materials. In ML, HEC-SML will motivate the design of strategies to impose physics constraints and develop optimization methods for training more efficient algorithms. Combined research in these disparate fields creates new objectives that motivate transformative advances in each discipline. The research enabled by HEC-SML will enable advances to and train scientists that can address edge computing challenges for wireless communication, healthcare monitoring, advanced manufacturing, and multi-agent autonomous systems. The instrument will also enable several student curriculum advances along this convergence of fields. This program will engage interdisciplinary researchers in biological sciences, materials science, machine learning, and heterogeneous computing and lead to novel research thrusts. High-performance computing (HPC) has made tremendous advances in scheduled, parallelized, and distributed computing. Experimental microscopy requires that voluminous data at high velocity is processed in timescales relevant to the experiment (nanoseconds-minutes). HPC facilities cannot meet these needs as they are not designed for dedicated networking and computation and typically do not have heterogeneous compute nodes for low-latency computation. HEC-SML will be a purpose-built instrument for real-time analysis and control of microscopy. A technical innovation is co-locating HPC with microscopy to enable low-cost, reconfigurable, dedicated high-speed networking. With this, we will develop a centralized edge computing platform for signal processing, data reduction, and ML tools for many microscopy modalities. The instrument will provide a platform for real-time analysis and control of optical, scanning probe, and transmission electron microscopy. HEC-SML will provide new capabilities for counting and sorting cells as well as for the characterization and manipulation of materials for energy conversion, sensing, and quantum materials. HEC-SML will provide a turn-key solution for microscopy and other data-intensive scientific experiments. Furthermore, HEC-SML will enable transformative advances in personalized medicine; sensing, energy conversion, and quantum materials; scientific and physics-informed ML methods using experimental data; and codesign of heterogeneous computing and ML for low-latency data reduction, scientific signal processing, and control systems.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.

该项目旨在开发一个异构边缘计算平台，用于极端边缘的实时科学机器学习（HEC-SML）。这样的平台将允许光学、扫描探针和透射电子显微镜的实时分析和控制。将计算放在边缘-尽可能靠近数据源-在将数据发送到高性能计算设施时规避了延迟和带宽挑战，并将使实时数据分析能够进行具有创造性探究的科学实验。HEC-SML的发展将导致显微镜，机器学习（ML）和异构计算概念的融合。在显微镜方面，先进的控制系统将使新的成像模式成为可能;个性化医疗方法;以及发现和理解功能和量子材料。在机器学习中，HEC-SML将激励策略的设计，以施加物理约束，并开发优化方法来训练更有效的算法。在这些不同领域的综合研究创造了新的目标，激励每个学科的变革性进步。HEC-SML支持的研究将推动和培训科学家，以解决无线通信、医疗监测、先进制造和多智能体自主系统的边缘计算挑战。该工具还将使几个学生的课程进展沿着这一领域的融合。该计划将吸引生物科学，材料科学，机器学习和异构计算的跨学科研究人员，并导致新的研究方向。高性能计算（HPC）在调度、并行和分布式计算方面取得了巨大的进步。实验显微镜需要大量的数据在高速处理的时间尺度相关的实验（纳秒分钟）。HPC设施无法满足这些需求，因为它们不是为专用网络和计算而设计的，并且通常没有用于低延迟计算的异构计算节点。HEC-SML将是一种专门用于实时分析和控制显微镜的仪器。一项技术创新是将HPC与显微镜共存，以实现低成本、可重新配置的专用高速网络。有了这个，我们将开发一个集中的边缘计算平台，用于许多显微镜模式的信号处理，数据简化和ML工具。该仪器将为光学、扫描探针和透射电子显微镜的实时分析和控制提供平台。HEC-SML将为细胞计数和分选以及能量转换、传感和量子材料的表征和操作提供新的能力。HEC-SML将为显微镜和其他数据密集型科学实验提供交钥匙解决方案。此外，HEC-SML将实现个性化医疗的变革性进展;传感，能量转换和量子材料;使用实验数据的科学和物理学信息ML方法;以及异构计算和ML的协同设计，用于低延迟数据简化，科学信号处理，该奖项反映了NSF的法定使命，并被认为是值得通过评估使用基金会的知识优点和更广泛的影响审查标准。

项目成果

Dual Label-Guided Graph Refinement for Multi-View Graph Clustering

• DOI: 10.1609/aaai.v37i7.26057
• 发表时间: 2023-06
• 作者: Yawen Ling;Jianpeng Chen;Yazhou Ren;X. Pu;Jie Xu;Xiao-lan Zhu;Lifang He

Hierarchical State Abstraction Based on Structural Information Principles

• DOI: 10.48550/arxiv.2304.12000
• 发表时间: 2023-04
• 作者: Xianghua Zeng;Hao Peng;Angsheng Li;Chunyang Liu;Lifang He;Philip S. Yu

Self-Supervised Graph Attention Networks for Deep Weighted Multi-View Clustering

• DOI: 10.1609/aaai.v37i7.25960
• 发表时间: 2023-06
• 作者: Zongmo Huang;Yazhou Ren;X. Pu;Shudong Huang;Zenglin Xu;Lifang He

One-shot Joint Extraction, Registration and Segmentation of Neuroimaging Data

• DOI: 10.1145/3580305.3599452
• 发表时间: 2023-07
• 期刊: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
• 作者: Yao Su;Zhentian Qian;Lei Ma;Lifang He;Xiangnan Kong

Comprehensive Multiview Representation Learning via Deep Autoencoder-Like Nonnegative Matrix Factorization

• DOI: 10.1109/tnnls.2023.3304626
• 发表时间: 2023-09
• 期刊: IEEE Transactions on Neural Networks and Learning Systems
• 影响因子: 10.4
• 作者: Haonan Huang;Guoxu Zhou;Qianchuan Zhao;Lifang He;Shengli Xie