Results for “Machine Learning Hardware” — Page 23

DOE OSTI2025

MLCommons Science Benchmarks

Benchmarks are a cornerstone of modern machine learning practice, providing standardized eval- uations that enable reproducibility, comparison, and scientific progress. Yet, as AI systems particularly deep learning models become increasingly dynamic, traditional static benchmarking approaches are losing their relevance. Models rapidly evolve in architecture, scale, and capability; datasets shift; and deployment contexts continuously change, creating a moving target for evaluation. Without adaptive benchmarking frame- works, both scientific assessment and real-world de- ployment risk becoming misaligned with actual system behavior. Drawing on our experience from MLCommons, educa- tional initiatives, and government programs such as the DOE s Million Parameter Consortium, we identify key barriers that hinder the broader adoption and utility of benchmarking in AI. These include substantial resource demands, limited access to specialized hardware, lack of expertise in benchmark design, and uncertainty among practitioners about how to relate benchmark results to their own application domains. Moreover, current benchmarks often emphasize peak performance on leadership-class hardware, offering limited guidance for more diverse, real-world deployment scenarios. We argue that benchmarking itself must become dy- namic in order to incorporate evolving models, updated data, and heterogeneous computational platforms while maintaining transparency, reproducibility, and inter- pretability. Democratizing this process requires not only technical innovation, but also systematic educational efforts spanning undergraduate to professional levels to develop sustained expertise in benchmark design and use. Finally, benchmarks should be framed and com- municated to support application-relevant comparisons, enabling both developers and users to make informed, context-sensitive decisions. Advancing dynamic and inclusive benchmarking practices will be essential to ensure that evaluation keeps pace with the evolving AI landscape and supports responsible, reproducible, and accessible AI deployment.

Hawks, Benjamin G. [Fermilab]↗

NASA NTRSDate not supplied

Space Flown Rodent Liver RNA Sequencing Data for Machine Learning in Space Biology Research

High-throughput nucleic acid sequencing (DNA-seq, RNA-seq) has become widespread in biomedical research due to the growing availability and affordability of these assays. Data analysis has been accelerated in recent years by the adoption of artificial intelligence (AI) and machine learning (ML) techniques by biomedical researchers. In space biology research, RNAseq datasets from space-flown experimental samples are critical for characterizing the gene expression aberrations associated with exposure to spaceflight stressors. However, space biological experiments tend to be very low sample size, so identifying proper AI/ML algorithms for sequencing data analysis is an ongoing challenge since these algorithms typically require large sample size. The NASA Science Mission Directorate (SMD) has started the “Benchmark Initiative for AI/ML”, focused on creating datasets meant for three main applications: 1) scientific benchmarking, which finds the best algorithm for a specific problem; 2) application benchmarking, which measures algorithm performance against a set of parameters; and 3) system benchmarking, which evaluates performance of hardware and software architecture. These scientific benchmarks consist of an AI-ready dataset and a reference implementation on a specific scientific question. In this work, we focused on generating standardized datasets to allow the scientific community to benchmark AI/ML algorithms in the domain of space biology. We present here a standardized, AI-ready, publicly available benchmark dataset for space biology RNA-seq data as a collaboration between the NASA AI4LS (Artificial Intelligence for Life Sciences) working group. and NASA’s SMD. This dataset consists of space-flown and ground control mouse liver found in the NASA GeneLab omics database. However, to amplify the small sample number (n=112 samples) for ML purposes, we employ Gaussian noise and a generative adversarial network to extend this dataset to 6,000 synthetic samples, matching the original gene expression characteristics.

James Casaletto↗

DOE OSTI2019

MetaStrider: Architectures for Scalable Memory-centric Reduction of Sparse Data Streams

Reduction is an operation performed on the values of two or more key-value pairs that share the same key. Reduction of sparse data streams finds application in a wide variety of domains such as data and graph analytics, cybersecurity, machine learning, and HPC applications. However, these applications exhibit low locality of reference, rendering traditional architectures and data representations inefficient. This article presents MetaStrider, a significant algorithmic and architectural enhancement to the state-of-the-art, SuperStrider. Furthermore, these enhancements enable a variety of parallel, memory-centric architectures that we propose, resulting in demonstrated performance that scales near-linearly with available memory-level parallelism.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

The Artificial Scientist: in-Transit Machine Learning of Plasma Simulations

Large-scale simulations or scientific experiments produce petabytes of data per run. This poses massive challenges for I/O and storage when scientific analysis workflows are run manually offline. Unsupervised deep learning-based techniques to extract patterns and non-linear relations from these large amounts of data provide a way to build scientific understanding from raw data, reducing the need for manual pre-selection of analysis steps, but require exascale compute and memory to process the full dataset available. In this paper, we demonstrate a heterogeneous streaming workflow in which plasma simulation data is streamed directly to a Machine Learning (ML) application training a model on the simulation data in-transit, completely circumventing the capacity-constrained filesystem bottleneck. This workflow employs openPMD to provide a high level interface to describe scientific data and also uses ADIOS2, to transfer volumes of data that exceed the capabilities of the filesystem. We employ experience replay to avoid catastrophic forgetting in learning from this non-steady state process in a continual manner and adapt it to improve model convergence while learning in-transit. As a proof-of-concept, we approach the ill-posed inverse problem of predicting particle dynamics from radiation in a particle-incell (PIConGPU) simulation of the Kelvin-Helmholtz instability (KHI). We detail hardware-software co-design challenges as we scale PIConGPU to full Frontier, the Top-1 system as of June 2024 Top500 list.

Kelling, Jeffrey [Helmholtz-Zentrum Dresden Rossen↗

DOE OSTI2015

MO-B-BRD-02: 3D Printing in the Clinic

This session is designed so that the learning objectives are practical. The intent is that the attendee may take home an understanding of not just the technology, but also the logistical steps necessary to execute these 3D printing techniques in the clinic. Four practical 3D printing topics will be discussed: (i) Creating bolus and compensators for photon machines; (ii) tools for proton therapy; (iii) clinical applications in imaging; (iv) custom phantom design for clinic and research use. The use of 3D printers within the radiation oncology setting is proving to be a useful tool for creating patient specific bolus and compensators with the added benefit of cost savings. Creating the proper protocol is essential to ensuring that the desired effect is achieved and modeled in the treatment planning system. The critical choice of printer material (since it determines the interaction with the radiation) will be discussed. Selection of 3D printer type, design methods, verification of dose calculation, and the printing process will be detailed to give the basis for establishing your own protocol for electron and photon fields. A practical discussion of likely obstacles that may be encountered will be included. The diversity of systems and techniques in proton facilities leads to different facilities having very different requirements for beam modifying hardware and quality assurance devices. Many departments find the need to design and fabricate facility-specific equipment, making 3D printing an attractive technology. 3D printer applications in proton therapy will be discussed, including beam filters and compensators, and the design of proton therapy specific quality assurance tools. Quality control specific to 3D printing in proton therapy will be addressed. Advantages and disadvantages of different printing technology for these applications will also be discussed. 3D printing applications using high-resolution radiology-based imaging data will be presented. This data is used to 3D print individualized physical models of patient’s unique anatomy for aid in planning complex and challenging surgical procedures. Methods, techniques and imaging requirements for 3D printing anatomic models from imaging data will be discussed. Specific applications currently being used in the radiology clinic will be detailed. Standardized phantoms for radiation therapy are abundant. However, custom phantom designs can be advantageous for both clinical tasks and research. 3D printing is a useful method of custom fabrication that allows one to construct custom objects relatively quickly. Possibilities for custom radiotherapy phantoms range from 3D printing a hollow shell and filling the shell with tissue equivalent materials to fully printing the entire phantom with materials that are tissue equivalent as well as suitable for 3D printing. A range of materials available for use in radiotherapy phantoms and in the case of phantoms for dosimetric measurements, this choice is critical. The necessary steps required will be discussed including: modalities of 3D model generation, 3D model requirements for 3D printing, generation of machine instructions from the 3D model, and 3D printing techniques, choice of phantoms material, and troubleshooting techniques for each step in the process. Case examples of 3D printed phantoms will be shown. Learning Objectives: Understand the types of 3D modeling software required to design your device, the file formats required for data transfer from design software to 3D printer, and general troubleshooting techniques for each step of the process. Learn the differences between materials and design for photons vs. electrons vs. protons. Understand the importance of material choice and design geometries for your custom phantoms. Learn specific steps of quality assurance and quality control for 3D printed beam filters and compensators for proton therapy. Learn of special 3D printing applications for imaging. Cunha: Research support from Phillips Healthcare.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

MO-B-BRD-03: Principles, Pitfalls and Techniques of 3D Printing for Bolus and Compensators

This session is designed so that the learning objectives are practical. The intent is that the attendee may take home an understanding of not just the technology, but also the logistical steps necessary to execute these 3D printing techniques in the clinic. Four practical 3D printing topics will be discussed: (i) Creating bolus and compensators for photon machines; (ii) tools for proton therapy; (iii) clinical applications in imaging; (iv) custom phantom design for clinic and research use. The use of 3D printers within the radiation oncology setting is proving to be a useful tool for creating patient specific bolus and compensators with the added benefit of cost savings. Creating the proper protocol is essential to ensuring that the desired effect is achieved and modeled in the treatment planning system. The critical choice of printer material (since it determines the interaction with the radiation) will be discussed. Selection of 3D printer type, design methods, verification of dose calculation, and the printing process will be detailed to give the basis for establishing your own protocol for electron and photon fields. A practical discussion of likely obstacles that may be encountered will be included. The diversity of systems and techniques in proton facilities leads to different facilities having very different requirements for beam modifying hardware and quality assurance devices. Many departments find the need to design and fabricate facility-specific equipment, making 3D printing an attractive technology. 3D printer applications in proton therapy will be discussed, including beam filters and compensators, and the design of proton therapy specific quality assurance tools. Quality control specific to 3D printing in proton therapy will be addressed. Advantages and disadvantages of different printing technology for these applications will also be discussed. 3D printing applications using high-resolution radiology-based imaging data will be presented. This data is used to 3D print individualized physical models of patient’s unique anatomy for aid in planning complex and challenging surgical procedures. Methods, techniques and imaging requirements for 3D printing anatomic models from imaging data will be discussed. Specific applications currently being used in the radiology clinic will be detailed. Standardized phantoms for radiation therapy are abundant. However, custom phantom designs can be advantageous for both clinical tasks and research. 3D printing is a useful method of custom fabrication that allows one to construct custom objects relatively quickly. Possibilities for custom radiotherapy phantoms range from 3D printing a hollow shell and filling the shell with tissue equivalent materials to fully printing the entire phantom with materials that are tissue equivalent as well as suitable for 3D printing. A range of materials available for use in radiotherapy phantoms and in the case of phantoms for dosimetric measurements, this choice is critical. The necessary steps required will be discussed including: modalities of 3D model generation, 3D model requirements for 3D printing, generation of machine instructions from the 3D model, and 3D printing techniques, choice of phantoms material, and troubleshooting techniques for each step in the process. Case examples of 3D printed phantoms will be shown. Learning Objectives: Understand the types of 3D modeling software required to design your device, the file formats required for data transfer from design software to 3D printer, and general troubleshooting techniques for each step of the process. Learn the differences between materials and design for photons vs. electrons vs. protons. Understand the importance of material choice and design geometries for your custom phantoms. Learn specific steps of quality assurance and quality control for 3D printed beam filters and compensators for proton therapy. Learn of special 3D printing applications for imaging. Cunha: Research support from Phillips Healthcare.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

MO-B-BRD-04: Sterilization for 3D Printed Brachytherapy Applicators

This session is designed so that the learning objectives are practical. The intent is that the attendee may take home an understanding of not just the technology, but also the logistical steps necessary to execute these 3D printing techniques in the clinic. Four practical 3D printing topics will be discussed: (i) Creating bolus and compensators for photon machines; (ii) tools for proton therapy; (iii) clinical applications in imaging; (iv) custom phantom design for clinic and research use. The use of 3D printers within the radiation oncology setting is proving to be a useful tool for creating patient specific bolus and compensators with the added benefit of cost savings. Creating the proper protocol is essential to ensuring that the desired effect is achieved and modeled in the treatment planning system. The critical choice of printer material (since it determines the interaction with the radiation) will be discussed. Selection of 3D printer type, design methods, verification of dose calculation, and the printing process will be detailed to give the basis for establishing your own protocol for electron and photon fields. A practical discussion of likely obstacles that may be encountered will be included. The diversity of systems and techniques in proton facilities leads to different facilities having very different requirements for beam modifying hardware and quality assurance devices. Many departments find the need to design and fabricate facility-specific equipment, making 3D printing an attractive technology. 3D printer applications in proton therapy will be discussed, including beam filters and compensators, and the design of proton therapy specific quality assurance tools. Quality control specific to 3D printing in proton therapy will be addressed. Advantages and disadvantages of different printing technology for these applications will also be discussed. 3D printing applications using high-resolution radiology-based imaging data will be presented. This data is used to 3D print individualized physical models of patient’s unique anatomy for aid in planning complex and challenging surgical procedures. Methods, techniques and imaging requirements for 3D printing anatomic models from imaging data will be discussed. Specific applications currently being used in the radiology clinic will be detailed. Standardized phantoms for radiation therapy are abundant. However, custom phantom designs can be advantageous for both clinical tasks and research. 3D printing is a useful method of custom fabrication that allows one to construct custom objects relatively quickly. Possibilities for custom radiotherapy phantoms range from 3D printing a hollow shell and filling the shell with tissue equivalent materials to fully printing the entire phantom with materials that are tissue equivalent as well as suitable for 3D printing. A range of materials available for use in radiotherapy phantoms and in the case of phantoms for dosimetric measurements, this choice is critical. The necessary steps required will be discussed including: modalities of 3D model generation, 3D model requirements for 3D printing, generation of machine instructions from the 3D model, and 3D printing techniques, choice of phantoms material, and troubleshooting techniques for each step in the process. Case examples of 3D printed phantoms will be shown. Learning Objectives: Understand the types of 3D modeling software required to design your device, the file formats required for data transfer from design software to 3D printer, and general troubleshooting techniques for each step of the process. Learn the differences between materials and design for photons vs. electrons vs. protons. Understand the importance of material choice and design geometries for your custom phantoms. Learn specific steps of quality assurance and quality control for 3D printed beam filters and compensators for proton therapy. Learn of special 3D printing applications for imaging. Cunha: Research support from Phillips Healthcare.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

MO-B-BRD-00: Clinical Applications of 3D Printing

This session is designed so that the learning objectives are practical. The intent is that the attendee may take home an understanding of not just the technology, but also the logistical steps necessary to execute these 3D printing techniques in the clinic. Four practical 3D printing topics will be discussed: (i) Creating bolus and compensators for photon machines; (ii) tools for proton therapy; (iii) clinical applications in imaging; (iv) custom phantom design for clinic and research use. The use of 3D printers within the radiation oncology setting is proving to be a useful tool for creating patient specific bolus and compensators with the added benefit of cost savings. Creating the proper protocol is essential to ensuring that the desired effect is achieved and modeled in the treatment planning system. The critical choice of printer material (since it determines the interaction with the radiation) will be discussed. Selection of 3D printer type, design methods, verification of dose calculation, and the printing process will be detailed to give the basis for establishing your own protocol for electron and photon fields. A practical discussion of likely obstacles that may be encountered will be included. The diversity of systems and techniques in proton facilities leads to different facilities having very different requirements for beam modifying hardware and quality assurance devices. Many departments find the need to design and fabricate facility-specific equipment, making 3D printing an attractive technology. 3D printer applications in proton therapy will be discussed, including beam filters and compensators, and the design of proton therapy specific quality assurance tools. Quality control specific to 3D printing in proton therapy will be addressed. Advantages and disadvantages of different printing technology for these applications will also be discussed. 3D printing applications using high-resolution radiology-based imaging data will be presented. This data is used to 3D print individualized physical models of patient’s unique anatomy for aid in planning complex and challenging surgical procedures. Methods, techniques and imaging requirements for 3D printing anatomic models from imaging data will be discussed. Specific applications currently being used in the radiology clinic will be detailed. Standardized phantoms for radiation therapy are abundant. However, custom phantom designs can be advantageous for both clinical tasks and research. 3D printing is a useful method of custom fabrication that allows one to construct custom objects relatively quickly. Possibilities for custom radiotherapy phantoms range from 3D printing a hollow shell and filling the shell with tissue equivalent materials to fully printing the entire phantom with materials that are tissue equivalent as well as suitable for 3D printing. A range of materials available for use in radiotherapy phantoms and in the case of phantoms for dosimetric measurements, this choice is critical. The necessary steps required will be discussed including: modalities of 3D model generation, 3D model requirements for 3D printing, generation of machine instructions from the 3D model, and 3D printing techniques, choice of phantoms material, and troubleshooting techniques for each step in the process. Case examples of 3D printed phantoms will be shown. Learning Objectives: Understand the types of 3D modeling software required to design your device, the file formats required for data transfer from design software to 3D printer, and general troubleshooting techniques for each step of the process. Learn the differences between materials and design for photons vs. electrons vs. protons. Understand the importance of material choice and design geometries for your custom phantoms. Learn specific steps of quality assurance and quality control for 3D printed beam filters and compensators for proton therapy. Learn of special 3D printing applications for imaging. Cunha: Research support from Phillips Healthcare.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

MO-B-BRD-01: Creation of 3D Printed Phantoms for Clinical Radiation Therapy

This session is designed so that the learning objectives are practical. The intent is that the attendee may take home an understanding of not just the technology, but also the logistical steps necessary to execute these 3D printing techniques in the clinic. Four practical 3D printing topics will be discussed: (i) Creating bolus and compensators for photon machines; (ii) tools for proton therapy; (iii) clinical applications in imaging; (iv) custom phantom design for clinic and research use. The use of 3D printers within the radiation oncology setting is proving to be a useful tool for creating patient specific bolus and compensators with the added benefit of cost savings. Creating the proper protocol is essential to ensuring that the desired effect is achieved and modeled in the treatment planning system. The critical choice of printer material (since it determines the interaction with the radiation) will be discussed. Selection of 3D printer type, design methods, verification of dose calculation, and the printing process will be detailed to give the basis for establishing your own protocol for electron and photon fields. A practical discussion of likely obstacles that may be encountered will be included. The diversity of systems and techniques in proton facilities leads to different facilities having very different requirements for beam modifying hardware and quality assurance devices. Many departments find the need to design and fabricate facility-specific equipment, making 3D printing an attractive technology. 3D printer applications in proton therapy will be discussed, including beam filters and compensators, and the design of proton therapy specific quality assurance tools. Quality control specific to 3D printing in proton therapy will be addressed. Advantages and disadvantages of different printing technology for these applications will also be discussed. 3D printing applications using high-resolution radiology-based imaging data will be presented. This data is used to 3D print individualized physical models of patient’s unique anatomy for aid in planning complex and challenging surgical procedures. Methods, techniques and imaging requirements for 3D printing anatomic models from imaging data will be discussed. Specific applications currently being used in the radiology clinic will be detailed. Standardized phantoms for radiation therapy are abundant. However, custom phantom designs can be advantageous for both clinical tasks and research. 3D printing is a useful method of custom fabrication that allows one to construct custom objects relatively quickly. Possibilities for custom radiotherapy phantoms range from 3D printing a hollow shell and filling the shell with tissue equivalent materials to fully printing the entire phantom with materials that are tissue equivalent as well as suitable for 3D printing. A range of materials available for use in radiotherapy phantoms and in the case of phantoms for dosimetric measurements, this choice is critical. The necessary steps required will be discussed including: modalities of 3D model generation, 3D model requirements for 3D printing, generation of machine instructions from the 3D model, and 3D printing techniques, choice of phantoms material, and troubleshooting techniques for each step in the process. Case examples of 3D printed phantoms will be shown. Learning Objectives: Understand the types of 3D modeling software required to design your device, the file formats required for data transfer from design software to 3D printer, and general troubleshooting techniques for each step of the process. Learn the differences between materials and design for photons vs. electrons vs. protons. Understand the importance of material choice and design geometries for your custom phantoms. Learn specific steps of quality assurance and quality control for 3D printed beam filters and compensators for proton therapy. Learn of special 3D printing applications for imaging. Cunha: Research support from Phillips Healthcare.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

AEcroscopy: A Software–Hardware Framework Empowering Microscopy Toward Automated and Autonomous Experimentation

Microscopy has been pivotal in improving the understanding of structure-function relationships at the nanoscale and is by now ubiquitous in most characterization labs. However, traditional microscopy operations are still limited largely by a human-centric click-and-go paradigm utilizing vendor-provided software, which limits the scope, utility, efficiency, effectiveness, and at times reproducibility of microscopy experiments. Here, in this work, a coupled software–hardware platform is developed that consists of a software package termed AEcroscopy (short for Automated Experiments in Microscopy), along with a field-programmable-gate-array device with LabView-built customized acquisition scripts, which overcome these limitations and provide the necessary abstractions toward full automation of microscopy platforms. The platform works across multiple vendor devices on scanning probe microscopes and electron microscopes. It enables customized scan trajectories, processing functions that can be triggered locally or remotely on processing servers, user-defined excitation waveforms, standardization of data models, and completely seamless operation through simple Python commands to enable a plethora of microscopy experiments to be performed in a reproducible, automated manner. This platform can be readily coupled with existing machine-learning libraries and simulations, to provide automated decision-making and active theory-experiment optimization to turn microscopes from characterization tools to instruments capable of autonomous model refinement and physics discovery.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Modeling performance of data collection systems for high-energy physics

Exponential increases in scientific experimental data are outpacing silicon technology progress, necessitating heterogeneous computing systems—particularly those utilizing machine learning (ML)—to meet future scientific computing demands. The growing importance and complexity of heterogeneous computing systems require systematic modeling to understand and predict the effective roles for ML. We present a model that addresses this need by framing the key aspects of data collection pipelines and constraints and combining them with the important vectors of technology that shape alternatives, computing metrics that allow complex alternatives to be compared. For instance, a data collection pipeline may be characterized by parameters such as sensor sampling rates and the overall relevancy of retrieved samples. Alternatives to this pipeline are enabled by development vectors including ML, parallelization, advancing CMOS, and neuromorphic computing. By calculating metrics for each alternative such as overall F1 score, power, hardware cost, and energy expended per relevant sample, our model allows alternative data collection systems to be rigorously compared. We apply this model to the Compact Muon Solenoid experiment and its planned high luminosity-large hadron collider upgrade, evaluating novel technologies for the data acquisition system (DAQ), including ML-based filtering and parallelized software. The results demonstrate that improvements to early DAQ stages significantly reduce resources required later, with a power reduction of 60% and increased relevant data retrieval per unit power (from 0.065 to 0.31 samples/kJ). However, we predict that further advances will be required in order to meet overall power and cost constraints for the DAQ.

Olin-Ammentorp, Wilkie (ORCID:0000000224729862)↗

DOE OSTI2022

Strategies for Integrating Deep Learning Surrogate Models with HPC Simulation Applications

The emerging trend of the convergence of high performance computing (HPC), machine learning/deep learning (ML/DL), and big data analytics presents a host of challenges for large-scale computing campaigns that seek best practices to interleave traditional scientific simulation-based workloads with ML/DL models. A portfolio of systematic approaches to incorporate deep learning into modeling and simulation serves a vital need when we support AI for science at a computing facility. In this paper, we evaluate several strategies for deploying deep learning surrogate models in a representative physics application on supercomputers at the Oak Ridge Leadership Computing Facility (OLCF). We discuss a set of recommended deployment architectures and implementation approaches. We analyze and evaluate these alternatives and show their performance and scalability up to 1000 GPUs on two mainstream platforms equipped with different deep learning hardware and software stacks.

Yin, Junqi↗

DOE OSTI2023

Out-of-distribution generalization for learning quantum dynamics

Abstract Generalization bounds are a critical tool to assess the training data requirements of Quantum Machine Learning (QML). Recent work has established guarantees for in-distribution generalization of quantum neural networks (QNNs), where training and testing data are drawn from the same data distribution. However, there are currently no results on out-of-distribution generalization in QML, where we require a trained model to perform well even on data drawn from a different distribution to the training distribution. Here, we prove out-of-distribution generalization for the task of learning an unknown unitary. In particular, we show that one can learn the action of a unitary on entangled states having trained only product states. Since product states can be prepared using only single-qubit gates, this advances the prospects of learning quantum dynamics on near term quantum hardware, and further opens up new methods for both the classical and quantum compilation of quantum circuits.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Out-of-distribution generalization for learning quantum dynamics

Generalization bounds are a critical tool to assess the training data requirements of Quantum Machine Learning (QML). Recent work has established guarantees for in-distribution generalization of quantum neural networks (QNNs), where training and testing data are drawn from the same data distribution. However, there are currently no results on out-of-distribution generalization in QML, where we require a trained model to perform well even on data drawn from a different distribution to the training distribution. Here, we prove out-of-distribution generalization for the task of learning an unknown unitary. In particular, we show that one can learn the action of a unitary on entangled states having trained only product states. Since product states can be prepared using only single-qubit gates, this advances the prospects of learning quantum dynamics on near term quantum hardware, and further opens up new methods for both the classical and quantum compilation of quantum circuits.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRSDate not supplied

Developing Methods for Exercise System Kinematics Tracking

BACKGROUND How to quantify the load and forces produced by exercise equipment and their Vibration Isolation and Stabilization (VIS) platforms in-flight is an active area of investigation. Kinematic tracking paired with system modeling can provide insights as well as verification and validation of simulations used for system design and development. Traditional motion capture methods can require significant cost in equipment procurement and crew-time, but newer lessons learned can be leveraged [1]. The VIS systems of current and future exercise hardware on the International Space Station (ISS) such as the Cycle Ergometer with Vibration Isolation System (CEVIS) and the European Enhanced Exploration Exercise Device (E4D) are not currently outfitted with IMUs or similar measurement devices. Video-based methods would enable use of multi-purpose, crew-familiar flight equipment. An initial exploration of video-based solutions was performed utilizing 2-camera video from crew cycling on Teal-CEVIS on the ISS. METHODS AND RESULTS Our group has scoped a variety of video-based object tracking methods. To date, we have primarily investigated computer vision toolkits such as openCV. Techniques explored include key-point detection, background subtraction, region-of interest tracking, color-based tracking, tag masking and tracking, and corner detection. Although object-tracking and 6D pose estimation is a rich field, space applications are a unique problem that are challenging for existing software and toolkits. The majority of the existing object-tracking applications involve vehicles/pedestrians and household objects with simple backgrounds. We have identified the following features which pose particular challenges for on-station exercise equipment tracking: Busy and visually cluttered background Low-textured tracking object with relatively small motions Occlusions and motion by human subject and loose, floating objects Limited number of video cameras with no fixed global references Limited ability to add tags, markers, or visual references to the tracking object Lack of training data for Machine Learning (ML) algorithms CONCLUSION We will summarize the efficacy of techniques tested for a ground mock-trial and the on-station exercise trial. It is likely that human-in-loop feedback or a conglomerate of methods is required. ML-based methods, like those implemented for human body tracking [2], may still be a viable option, but more training data and validation is needed.

L B Nilsson↗

DOE OSTI2024

The future of self-driving laboratories: from human in the loop interactive AI to gamification

Recent developments in artificial intelligence (AI) and machine learning (ML), implemented through self-driving laboratories (SDLs), are rapidly creating unprecedented opportunities for the accelerated discovery and optimization of materials. This paper provides a joint analysis of SDLs from both academic and industry perspectives, highlighting the importance of integrating human intelligence in these systems. It discusses the necessity of careful planning in SDL design across physical, data, and workflow dimensions, including instrumental setup, experimental workflow, data management, and human–SDL interaction. The significance of integrating human input within SDLs, especially as the focus shifts from individual tools and tasks to the creation and management of complex workflows, is emphasized. The paper stresses on the crucial role of reward function design in developing forward-looking workflows and examines the interplay between hardware evolution, ML application across chemical processes, and the influence of reward systems in research. Ultimately, the article advocates for a future where SDLs blend human intuition in hypothesis formulation with AI's precision, speed, and data-handling capabilities.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Randomized Algorithms for Scientific Computing (RASC)

Randomized algorithms have propelled advances in artificial intelligence (AI) and represent a foundational research area in advancing AI for Science. Future advancements in DOE Office of Science priority areas such as climate science, astrophysics, fusion, advanced materials, combustion, and quantum computing all require randomized algorithms for surmounting challenges of complexity, robustness, and scalability. Advances in data collection and numerical simulation have changed the dynamics of scientific research and motivate the need for randomized algorithms. For instance, advances in imaging technologies such as X-ray ptychography, electron microscopy, electron energy loss spectroscopy, or adaptive optics lattice light-sheet microscopy collect hyperspectral imaging and scattering data in terabytes, at breakneck speed enabled by state-of-the-art detectors. The data collection is exceptionally fast compared with its analysis. Likewise, advances in high-performance architectures have made exascale computing a reality and changed the economies of scientific computing in the process. Floating-point operations that create data are essentially free in comparison with data movement. Thus far, most approaches have focused on creating faster hardware. Ironically, this faster hardware has exacerbated the problem by making data still easier to create. Under such an onslaught, scientists often resort to heuristic deterministic sampling schemes (e.g., low-precision arithmetic, sampling every nth element) and sacrifice potentially valuable accuracy. Dramatically better results can be achieved via randomized algorithms, reducing the data size as much as or more than naive deterministic subsampling can achieve, while retaining the high accuracy of computing on the full data set. By randomized algorithms we mean those algorithms that employ some form of randomness in internal algorithmic decisions to accelerate time to solution, increase scalability, or improve reliability. Examples include matrix sketching for solving large-scale least-squares problems (see Figure 1) and stochastic gradient descent for training machine learning models. We are not recommending heuristic methods but rather randomized algorithms that have certificates of correctness and probabilistic guarantees of optimality and near-optimality. Such approaches can be useful beyond acceleration, for example, in understanding how to avoid measure zero worst-case scenarios that plague methods such as QR matrix factorization.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

SMC 2021 Data Challenge: Analyzing Resource Utilization and User Behavior on Titan Supercomputer

Resource utilization statistics of submitted jobs on a supercomputer can help us understand how users from various scientific domains use HPC platforms and better design a job scheduler. We explore to generate insight regarding workload distribution and usage pattern domains from job scheduler trace, GPU failure information, and project-specific information collected from Titan supercomputer. Furthermore, we want to know how the scheduler performance varies over time and how the users' scheduling behavior changes following a system failure. These observations have the potential to provide valuable insight, which is helpful to prepare for system failures. These practices will help us develop and apply novel machine learning algorithms in understanding system behavior, requirement, and better scheduling of HPC systems. There are two datasets, RUR and GPU: RUR dataset is the job scheduler traces collected from the Titan supercomputer from 01/01/2015 to 07/31/2019 (2015.csv - 2019.csv). These were collected using resource Utilization Report (RUR), a Cray-developed resource-usage data collection and reporting system. It contains the usage information of its critical resources (CPU, Memory, GPU, and I/O) of each running job on Titan during that period (https://ieeexplore.ieee.org/abstract/document/8891001). It includes ProjectAreas as additional information, every job is associated with a project ID. TheProjectAreas.csv dataset provides a mapping of the project ID to its domain science. GPU dataset has information regarding GPU failure on Titan. There have been some hardware-related issues in the GPUs in Titan that caused some GPUs to fail, sometimes irrecoverably during some job runs. This dataset provides information regarding these failures during the execution of the submitted jobs. GPUs on Titan are uniquely identified by a serial number (SN), and they are installed in a location. A GPU can be installed in a location, then removed from that location following a failure, and then re-installed in a different location after fixing the problem. If the failure can't be recovered, the GPU might be removed entirely from Titan. There are two prominent types of failures that resulted in the removal of GPUs from Titan: Double Bit Error (DBE) and Out of the Bus (OTB). The dataset (gc_full.csv) has seven attributes, we provided a short description of these attributes in the ReadMe file. To learn more about this dataset, please refer to the git repository https://github.com/olcf/TitanGPULife and the related publication (https://ieeexplore.ieee.org/abstract/document/9355319).

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