AI-Powered Knowledge Graphs for Neuromorphic and Energy-Efficient Computing
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Gautam, Ashish [Oak Ridge National Laboratory]↗
SEARCH · Engineering Papers
Results for “AI efficiency”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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AOI.1 Application of Artificial Intelligence techniques enabling coal fired power plants the ability to achieve higher efficiency, improved availability, and increased reliability of their operations (Final Report)
During this effort, SparkCognition with support from the Electric Power Research Institute (EPRI) was tasked with applying artificial intelligence (AI) to improve the reliability, efficiency, and safety of operations at a coal-fired plant. By implementing AI techniques, like machine learning (ML), it is believed that operators can leverage existing data sources to gain more insights such as advanced warning of machine degradation. With enough lead time, a reliability engineer can take action to minimize, or even avoid, impact to production. To complete this work effort, SparkCognition developed and refined an ML-based model using sensor data for a Steam Turbine unit at a host site. The models were deployed in an online, web-based solution that allows users to visualize model outputs and supporting data. The final solution, based on SparkCognition’s proprietary software platform called SparkPredict®, was shared with EPRI who completed an online evaluation of results to determine the solution’s ability to detect actionable events.
20 FOSSIL-FUELED POWER PLANTS↗
Intelligent Resource Management for Local Area Networks: Approach and Evolution
The Data Management System network is a complex and important part of manned space platforms. Its efficient operation is vital to crew, subsystems and experiments. AI is being considered to aid in the initial design of the network and to augment the management of its operation. The Intelligent Resource Management for Local Area Networks (IRMA-LAN) project is concerned with the application of AI techniques to network configuration and management. A network simulation was constructed employing real time process scheduling for realistic loads, and utilizing the IEEE 802.4 token passing scheme. This simulation is an integral part of the construction of the IRMA-LAN system. From it, a causal model is being constructed for use in prediction and deep reasoning about the system configuration. An AI network design advisor is being added to help in the design of an efficient network. The AI portion of the system is planned to evolve into a dynamic network management aid. The approach, the integrated simulation, project evolution, and some initial results are described.
Meike, Roger↗
A scalable framework for quantifying field-level agricultural carbon outcomes
Agriculture contributes nearly a quarter of global greenhouse gas (GHG) emissions, which is motivating interest in adopting certain farming practices that have the potential to reduce GHG emissions or sequester carbon in soil. The related GHG emission (including N 2 O and CH 4 ) and changes in soil carbon stock are defined here as “agricultural carbon outcomes”. Accurate quantification of agricultural carbon outcomes is the basis for achieving emission reductions for agriculture, but existing approaches for measuring carbon outcomes (including direct measurements, emission factors, and process-based modeling) fall short of achieving the required accuracy and scalability necessary to support credible, verifiable, and cost-effective measurement and improvement of these carbon outcomes. Here we propose a foundational and scalable framework to quantify field-level carbon outcomes for farmland, which is based on the holistic carbon balance of the agroecosystem: Agroecosystem Carbon Outcomes = Environment (E) × Management (M) × Crop (C). Following a comprehensive review of the scientific challenges associated with existing approaches, as well as their tradeoffs between cost and accuracy, we propose that the most viable path for the quantification of field-level carbon outcomes in agricultural land is through an effective integration of various approaches (e.g. diverse observations, sensor/in-situ data, and modeling), defined as the “System-of-Systems” solution. Such a “System-of-Systems” solution should simultaneously comprise the following components: (1) scalable collection of ground truth data and cross-scale sensing of environment variables (E), management practices (M), and crop conditions (C) at the local field level; (2) advanced modeling with necessary processes to support the quantification of carbon outcomes; (3) systematic Model-Data Fusion (MDF), i.e. robust and efficient methods to integrate sensing data and models at each local farmland level; (4) high computation efficiency and artificial intelligence (AI) to scale to millions of individual fields with low cost; and (5) robust and multi-tier validation systems and infrastructures to ensure solution fidelity and true scalability, i.e. the ability of a solution to perform robustly with accepted accuracy on all targeted fields. In this regard, we provide here the detailed scientific rationale, current progress, and future research and development (R&D) priorities to achieve different components of the “System-of-Systems” solution, thus accomplishing the Environment×Management×Crop framework to quantify field-level agricultural carbon outcomes.
54 ENVIRONMENTAL SCIENCES↗
The neurobench framework for benchmarking neuromorphic computing algorithms and systems
Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accurately measure technological advancements, compare performance with conventional methods, and identify promising future research directions. This article presents NeuroBench, a benchmark framework for neuromorphic algorithms and systems, which is collaboratively designed from an open community of researchers across industry and academia. NeuroBench introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent and hardware-dependent settings. For latest project updates, visit the project website (neurobench.ai).
Yik, Jason [Harvard Univ., Cambridge, MA (United S↗
Shared resource control between human and computer
The advantages of an AI system of actively monitoring human control of a shared resource (such as a telerobotic manipulator) are presented. A system is described in which a simple AI planning program gains efficiency by monitoring human actions and recognizing when the actions cause a change in the system's assumed state of the world. This enables the planner to recognize when an interaction occurs between human actions and system goals, and allows maintenance of an up-to-date knowledge of the state of the world and thus informs the operator when human action would undo a goal achieved by the system, when an action would render a system goal unachievable, and efficiently replans the establishment of goals after human intervention.
Hendler, James↗
Computational methods for efficient structural reliability and reliability sensitivity analysis
This paper presents recent developments in efficient structural reliability analysis methods. The paper proposes an efficient, adaptive importance sampling (AIS) method that can be used to compute reliability and reliability sensitivities. The AIS approach uses a sampling density that is proportional to the joint PDF of the random variables. Starting from an initial approximate failure domain, sampling proceeds adaptively and incrementally with the goal of reaching a sampling domain that is slightly greater than the failure domain to minimize over-sampling in the safe region. Several reliability sensitivity coefficients are proposed that can be computed directly and easily from the above AIS-based failure points. These probability sensitivities can be used for identifying key random variables and for adjusting design to achieve reliability-based objectives. The proposed AIS methodology is demonstrated using a turbine blade reliability analysis problem.
Wu, Y.-T.↗
EFFICIENT AND ECONOMIC HYDRAULIC FRACTURE DIAGNOSTICS USING METAMATERIALS AND EDGE AI
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02 PETROLEUM↗
Using Artificial Intelligence to Improve Reliability and Operational Efficiency of Small-Scale Hydroelectric Distributed Generation
Reliability and resilience are critical concerns for distributed generation (DG) at the rural electric level. The integration of renewable energy sources, such as small-scale hydroelectric distributed generators (hydro DGs), introduces operational challenges, particularly regarding aging infrastructure and grid stability. Artificial Intelligence (AI)-driven Machine Learning (ML) models and applications of Large Language Models (LLMs) offer promising solutions for optimizing DG operations and enhancing resilience. This paper explores AI-based models for improving efficiency, fault resolution, and outage mitigation in small-scale hydro DGs. Furthermore, it highlights the development of a centralized, AI-powered information portal for rural electric cooperatives and municipalities. The research evaluates hydro DG plant models and discusses the applicability of AI-powered question-answering tools for real-time operations, focusing on statistical data, load flow, voltage regulation, and generation power. The findings demonstrate AI’s potential to transform DG management to ensure greater stability and resilience in rural electric grids.
Bhattacharyya, Arjun [ORNL] (ORCID:000900060976046↗
Intrinsically stretchable neuromorphic devices for on-body processing of health data with artificial intelligence
For leveraging wearable technologies to advance precision medicine, personalized and learning-based analysis of continuously acquired health data is indispensable, for which neuromorphic computing could provide the most efficient implementation of artificial intelligence (AI) data processing. For realizing on-body neuromorphic computing, skin-like stretchability is required, but yet to be combined with the suite of desired neuromorphic metrics, including linear, symmetric weight update, and sufficient state retention, for achieving high computing efficiency. Here, we report an intrinsically stretchable neuromorphic device based on an electrochemical transistor, which provides a large number (>800) of states, linear/symmetric weight update, excellent switching endurance (>100 million), good state retention (>10 4 s), together with high stretchability of 100% strain. Further integration into a prototype array successfully realized the implementation of vector-matrix multiplication even at 100% strain. Finally, we demonstrate the feasibility of implementing AI-based classification of health signals (as exemplified by electrocardiograms) with a high accuracy that is minimally influenced by the stretched state of the neuromorphic hardware. Finally, this work breaks the ground for combining AI data analysis into skin-like wearable electronics for achieving human-integrated/mimetic intelligent systems.
60 APPLIED LIFE SCIENCES↗
Green Computing Catalyzer Kickoff: Green Artificial Intelligence (AI) Directions
For the 2022 JISEA annual meeting, JISEA introduces in this presentation the new Green Computing Catalyzer. The new catalyzer looks to establish green computing as a salient research domain at NREL, and crucially, to begin to address the looming computing energy crisis. The presentation provides some basic numbers, establishing energy use and source or carbon emissions for computing. The presentation also includes a brief history of green computing at NREL, empirical deep learning efficiency study, and research directions.
AI efficiency↗
Multifunctional electrochemical memory stabilized by phase coexistence
Our growing computing needs, especially in applications that heavily rely on artificial intelligence (AI), motivate a search for new components that could substantially augment the performance of general-purpose digital computers. Beyond ON/OFF switching, new components with linear multistate analog resistive tuning, nonlinear volatile switching, spiking, oscillatory, stochastic and other complex functionalities could enable highly efficient neuromorphic computing schemes for AI information processing. Compared to the extreme multifunctionality of biological neurons, realizing all the above characteristics in a single, scalable analog component remains a grand challenge. Here we investigate electrochemical gating combined with localized thermal activation to program and switch a single, vertically integrated and dimensionally scaled electrothermal chemical random access memory (ETCRAM) with a channel and reservoir composed of phase-separated vanadium oxide. Closely related to electrochemical RAM (ECRAM), ETCRAM uses an integrated gate-heater electrode to overcome kinetic barriers that help retain states at ambient temperatures. In addition to synapse-like stable and programmable analog resistance states arising from redox-tunable phase coexistence, a single component exhibits neuron-like nonlinear conductance switching with a tunable threshold and self-driven dynamics owing to the thermally driven metal-insulator phase transition in vanadium dioxide. More broadly, we demonstrate that electrochemically stabilized phase coexistence could unlock analog electronics with novel functionality, stability, reconfigurability, and scalability.
Oh, Sangheon [Sandia National Lab. (SNL-CA), Liver↗
Advancing the Performance of Anion Exchange Membrane Electrolysis by Employing a Powder-Based Ionomer during Anode Catalyst Layer Fabrication
The performance of anion exchange membrane water electrolysis (AEMWE) can be significantly improved by utilizing powdered ionomers during the fabrication of the anode catalyst layer (CL) to modify the CL properties. When comparing powdered ionomers to dispersed ionomers across various catalysts including cobalt oxide (Co 3 O 4 ), nickel−iron oxide (NiFe 2 O 4 ), and iridium oxide (IrO 2 ) the anode fabricated with powdered ionomers demonstrates improved performance in polarization curves, enhanced charge transfer kinetics, and reduced ohmic and transport losses, as evidenced by voltage breakdown and electrochemical impedance spectroscopy analyses. Optimal performance is achieved using a Co 3 O 4 catalyst with a 10 wt % powdered ionomer via the catalystcoated substrate method. Microscopy analyses reveal that electrodes formed with powdered ionomers during fabrication exhibit a more uniform catalyst and ionomer distribution, increased porosity with smaller pore areas, improved electronic conduction with less catalyst agglomeration isolated by a nonconductive ionomer, and enhanced interfacial contact with the membrane and transport layer. These findings highlight that ionomers in a powdered form can promote beneficial properties and are a promising approach to improving AEMWE efficiency.
08 HYDROGEN↗
Position Papers for the ASCR Workshop on the Management and Storage of Scientific Data
The purpose of this workshop is to identify priority research directions in the area of data management for high-performance and scientific computing above and beyond HPC’s traditional "the parallel file system is the data-management system" model. Supporting the breadth of the DOE mission, including the explosion of AI uses and the growing needs of experimental and observational science, motivates revisiting our assumptions about data management. There are many facets of this topic to explore including: (1) Interfaces for accessing data that resides on traditional persistent storage as well as memory devices; (2) Storage-system architecture design that supports scientific workflows on varied hierarchical storage and networking devices; (3) Devising metadata management infrastructure to support FAIR principles (Findability, Accessibility, Interoperability, and Reusability); (4) Capturing provenance information about scientific data; (5) Utilizing AI to learn I/O patterns of emerging workloads for efficient data management; (6) Providing data management support for AI and complex workflows; and (7) Understanding the overlap between traditional storage systems and I/O (SSIO) efforts and data management. While the program committee has identified these topics as important areas for discussion, we welcome position papers from the community that propose additional topics of interest for discussion at the workshop. The workshop agenda will include breakout sessions for discussing these and selected topic areas to inform priority research directions for data management for high-performance and scientific computing.
97 MATHEMATICS AND COMPUTING↗
Report for the ASCR Workshop on the Management and Storage of Scientific Data
The purpose of this workshop is to identify priority research directions in the area of data management for high-performance and scientific computing above and beyond HPC’s traditional "the parallel file system is the data-management system" model. Supporting the breadth of the DOE mission, including the explosion of AI uses and the growing needs of experimental and observational science, motivates revisiting our assumptions about data management. There are many facets of this topic to explore including: (1) Interfaces for accessing data that resides on traditional persistent storage as well as memory devices; (2) Storage-system architecture design that supports scientific workflows on varied hierarchical storage and networking devices; (3) Devising metadata management infrastructure to support FAIR principles (Findability, Accessibility, Interoperability, and Reusability); (4) Capturing provenance information about scientific data; (5) Utilizing AI to learn I/O patterns of emerging workloads for efficient data management; (6) Providing data management support for AI and complex workflows; and (7) Understanding the overlap between traditional storage systems and I/O (SSIO) efforts and data management. While the program committee has identified these topics as important areas for discussion, we welcome position papers from the community that propose additional topics of interest for discussion at the workshop. The workshop agenda will include breakout sessions for discussing these and selected topic areas to inform priority research directions for data management for high-performance and scientific computing.
97 MATHEMATICS AND COMPUTING↗
Scout: An AI-Driven Tool for Cyber Threat Report Creation
This talk will introduce Scout, an AI-driven tool designed to enhance the efficiency of cyber threat report creation. Attendees will learn how Scout leverages advanced AI technologies to streamline the reporting process, thereby enabling faster and more reliable threat assessments.
99 - GENERAL AND MISCELLANEOUS↗
Accelerating Next-Generation Cybersecurity R&D Using AI Workflows: BADGER Project Development
The Broadband Automation for Distributed Grid Efficiency and Resilience (BADGER) project aligns with national strategic priorities for integrating emerging wireless technologies and advancing AI-driven security. As critical infrastructure modernizes toward increasingly software-defined and interconnected systems, the ability to leverage 5G/NextG networks and AI-enabled control becomes essential. This report outlines work at the National Laboratory of the Rockies (NLR) to develop a NextG-native security architecture powered by AI-RAN concepts and evaluate workflows that enable efficient and reliable architectures. Together, these efforts position the laboratory to accelerate innovation while directly supporting national security and resilience objectives.
5G/6G↗
Privacy-Aware RAG-Enabled LLMs for Collaborative AI in Organizations
Recent advancements in Large Language Models (LLMs) based on Transformer architectures have significantly improved capabilities in natural language processing and generation. However, deploying LLMs for inter-organizational communication poses challenges, in ensuring privacy and facilitating effective collaboration. This paper introduces a novel decentralized inference meta-agent chatbot that leverages privacy-aware Retrieval-Augmented Generation (RAG)-enabled LLMs for collaborative AI communication across organizations. Built on Microsoft’s Autogen, the platform enables LLMs to autonomously refine responses, enhancing accuracy and relevance. It incorporates advanced hallucination mitigation techniques using Uptrain and a privacy-focused RAG framework that employs synthetic document generation to protect sensitive information. Comprehensive evaluations demonstrate the platform’s effectiveness in maintaining contextual relevance and stringent privacy standards, effectively addressing critical challenges in LLM-enhanced collaborative AI communication. This work represents a significant step toward secure and efficient inter-organizational collaboration using advanced generative AI technologies.
97 - MATHEMATICS AND COMPUTING↗