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

Lehigh University, Bethlehem PA

Investigators

Abstract

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.