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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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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Encoding integers and rationals on neuromorphic computers using virtual neuron

Abstract Neuromorphic computers emulate the human brain while being extremely power efficient for computing tasks. In fact, they are poised to be critical for energy-efficient computing in the future. Neuromorphic computers are primarily used in spiking neural network–based machine learning applications. However, they are known to be Turing-complete, and in theory can perform all general-purpose computation. One of the biggest bottlenecks in realizing general-purpose computations on neuromorphic computers today is the inability to efficiently encode data on the neuromorphic computers. To fully realize the potential of neuromorphic computers for energy-efficient general-purpose computing, efficient mechanisms must be devised for encoding numbers. Current encoding mechanisms (e.g., binning, rate-based encoding, and time-based encoding) have limited applicability and are not suited for general-purpose computation. In this paper, we present the virtual neuron abstraction as a mechanism for encoding and adding integers and rational numbers by using spiking neural network primitives. We evaluate the performance of the virtual neuron on physical and simulated neuromorphic hardware. We estimate that the virtual neuron could perform an addition operation using just 23 nJ of energy on average with a mixed-signal, memristor-based neuromorphic processor. We also demonstrate the utility of the virtual neuron by using it in some of the μ -recursive functions, which are the building blocks of general-purpose computation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

A mathematical framework for thermodynamic computing with applications to chemical reaction networks

The widespread adoption of energy-intensive computing applications has led to a growing need for energy-efficient computing approaches. Thermodynamic computing offers a promising approach for low-energy computation by leveraging the intrinsic computational capabilities of physical, chemical, or biological systems. However, the mathematical foundations of thermodynamic computing require further development to fully realize the potential energy efficiencies, as well as to assess factors like noise and operational speed. In this paper, we establish a mathematical framework for utilizing thermodynamic processes to perform fundamental operations, including addition, subtraction, multiplication, and division. We highlight the use of chemical reactions as potential computational units and explore synthetic chemical and biochemical systems as practical implementations. Additionally, we demonstrate how these principles can be applied to solving complex mathematical problems, such as ordinary differential equations (ODEs) and suggest the necessary components to implement the thermodynamic computing framework using chemical reactions based in a microfluidic device. This work enhances our understanding of thermodynamic processes for natural computing as a basis for scalable, energy-efficient computation in paradigm disruptive next-generation systems.

Cannon, William R. [Pacific Northwest National Lab↗
DOE OSTI2022

Quantum materials for energy-efficient neuromorphic computing: Opportunities and challenges

Neuromorphic computing approaches become increasingly important as we address future needs for efficiently processing massive amounts of data. The unique attributes of quantum materials can help address these needs by enabling new energy-efficient device concepts that implement neuromorphic ideas at the hardware level. In particular, strong correlations give rise to highly non-linear responses, such as conductive phase transitions that can be harnessed for short- and long-term plasticity. Similarly, magnetization dynamics are strongly non-linear and can be utilized for data classification. This Perspective discusses select examples of these approaches and provides an outlook on the current opportunities and challenges for assembling quantum-material-based devices for neuromorphic functionalities into larger emergent complex network systems.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Investigation of Exciton Delocalization and Exciton Coherence in Chromophores and Acoustic Nanostructures

Quantum materials have a rich potential for providing transformative energy technologies ranging from new energy efficient computation paradigms to lossless energy transport across the national grid. The field of quantum materials, however, is still in its infancy with many quantum materials systems requiring cyrogenic temperatures to be viable. Few viable, room-temperature quantum materials systems have been identified; therefore, identifying additional such systems is vitally important. This state of the science combined with the low number of potential systems that exhibit room-temperature quantum character make this field of research fertile ground for EPSCoR states (states eligible for funding under the Established Program to Stimulate Competitive Research program) such as Idaho to compete on a national level. As a result, this project proposed and accomplished two main goals in advancing two room-temperature quantum materials systems: (1) advance the science for demonstrating room temperature exciton delocalization in two excitonic systems and (2) build capacity and expertise in developing and understanding quantum materials at both Boise State University and Idaho National Laboratory. The work at Boise State focused mainly on the use of nucleic acid self-assembled chromophore molecule networks that offer a much easier and mature approach to construct aggregates of chromophores that can be tailored to specific two-dimensional and three-dimensional geometries and allow exciton manipulation towards achieving room-temperature quantum computation. A second complementary effort performed at Boise State and INL focused on confining excitons on quantum length scales using coherent surface acoustic phonons

36 MATERIALS SCIENCE↗
DOE OSTI2022

Neuromorphic scaling advantages for energy-efficient random walk computations

Neuromorphic computing, which aims to replicate the computational structure and architecture of the brain in synthetic hardware, has typically focused on artificial intelligence applications. What is less explored is whether such brain-inspired hardware can provide value beyond cognitive tasks. Here we show that the high degree of parallelism and configurability of spiking neuromorphic architectures makes them well suited to implement random walks via discrete-time Markov chains. Overall, these random walks are useful in Monte Carlo methods, which represent a fundamental computational tool for solving a wide range of numerical computing tasks. Using IBM’s TrueNorth and Intel’s Loihi neuromorphic computing platforms, we show that our neuromorphic computing algorithm for generating random walk approximations of diffusion offers advantages in energy-efficient computation compared with conventional approaches. We also show that our neuromorphic computing algorithm can be extended to more sophisticated jump-diffusion processes that are useful in a range of applications, including financial economics, particle physics and machine learning.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Sub-1K Cold-Electron Quantum Well Switching at Room Temperature

Quantum states can provide means to systematically manipulate the transport of electrons. Here, in this study, we present electron transport across quasi-bound states of two heterogeneous quantum wells (QWs), where the transport of thermally excited electrons is blocked or enabled depending on the relative positions of the two quasi-bound states, with an abrupt current onset occurring when the two QW states align. The QW switch comprises a source (Cr), QW1 (Cr 2 O 3 ), QW2 (SnO x , x < 2), a tunneling barrier (SiO 2 ), and a drain (Si), where the effective electron mass of QW1 (m* QW1 ) is selected to be larger than QW2 (m* QW2 ). The current-voltage (I-V) measurements of the fabricated devices show abrupt current onsets, with the current transition occurring within 0.25 mV, corresponding to an effective electron temperature of 0.8 K at room temperature. Since transistor power consumption is fundamentally tied to effective electron temperature, this sub-1K cold-electron QW switching holds promise for highly energy-efficient computing.

Fermi−Dirac thermal smearing↗
DOE OSTI2022

Microelectronics at the Department of Energy: Capabilities and Opportunities for Driving U.S. Competitiveness

Microelectronics are everywhere—embedded in our consumer products; underlying our power, transportation, and information infrastructure; and fortifying our national security. These and other microelectronics applications are also intricately linked with America’s energy and decarbonization goals. For instance, advanced power electronics are critical to our transition to renewable power and electric transport, and more energy-efficient computing and consumer devices are needed as energy consumption grows rapidly. Meanwhile, global supply chain disruptions beginning in 2020 and the ensuing chip shortage caused by the COVID-19 pandemic highlight the vulnerability of microelectronics supply chains. The globally distributed manufacturing base can be disrupted in unexpected ways, and there is no single solution to these challenges given the range of applications and technologies within the microelectronics industry—from ever-smaller, nanoscale feature sizes to large-scale commodity use cases to specialized functionalities. Rather, solutions will require the private sector, federal agencies, and research and development (R&D) organizations such as labs and universities to act in concert beyond their individual missions.

36 MATERIALS SCIENCE↗
DOE OSTI2023

An FPGA-Based Neuromorphic Processor with All-to-All Connectivity

Neuromorphic computing is a promising paradigm for future energy-efficient computing. At present, however, it is in its nascent stages—most hardware implementations are research-grade, commercial products are not available, and the software tools are not production-ready. The lack of hardware and software tools makes neuromorphic computing inaccessible to researchers around the globe. To this extent, we intend to build a low-cost, open-source, FPGA-based digital neuromorphic processor that can be used by researchers worldwide. In this paper, we present a preliminary implementation of the processor on a Xilinx Artix-7 FPGA using SystemVerilog. Our implementation supports the integrate-and-fire neuron with two parameters each for neurons and synapses. It also features all-to-all connectivity among neurons on the hardware. We test our implementation on four cases: bars and stripes datasets, shortest path algorithm, logic gates, and 8-3 encoder. We also perform a scalability study to understand the resource utilization of the FPGA as the number of all-to-all connected neurons increases. With our implementation, the Artix- 7 supports 65 neurons with all-to-all connectivity. Moreover, all the test cases mentioned above achieve 100% accuracy.

Maheshwari, Disha↗
DOE OSTI2026

Flexible AI Models for Grid Resilience

The rapid growth in size and complexity of artificial intelligence (AI) and machine learning (ML) models has led to increased energy demands, posing a threat to the reliability of the existing power grid. This project addresses the challenge of highly intermittent and energy-intensive inference workloads by (1) developing fidelity-adaptive neural networks capable of dynamic response to grid conditions and (2) integrating these networks with power flow simulations to assess their impact on power grid reliability. We will explore both top-down and bottom-up approaches to create hierarchies of submodels that provide a controlled trade-off between power draw and prediction accuracy. The top-down method utilizes NN pruning to reduce a flagship model into progressively smaller, energy-efficient variants. The bottom-up approach employs geometrically principled weight setting strategies to construct depth-efficient models from the ground up. A real-time hardware-in-the-loop (HIL) platform will be developed to simulate a scaled AC power grid, integrating live AI workload power draw and enabling dynamic model switching in response to grid feedback. This work will provide a novel framework for evaluating the impact of flexible AI/ML workloads on grid performance and establish new methodologies for energy-aware computing in data centers. The outcomes will demonstrate that adaptive AI/ML can play a critical role in improving grid stability while advancing NREL's leadership in energy-efficient computing research.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Controlled Formation of Conduction Channels in Memristive Devices Observed by X–ray Multimodal Imaging

Neuromorphic computing provides a means for achieving faster and more energy efficient computations than conventional digital computers for artificial intelligence (AI). However, its current accuracy is generally less than the dominant software-based AI. The key to improving accuracy is to reduce the intrinsic randomness of memristive devices, emulating synapses in the brain for neuromorphic computing. Here using a planar device as a model system, the controlled formation of conduction channels is achieved with high oxygen vacancy concentrations through the design of sharp protrusions in the electrode gap, as observed by X-ray multimodal imaging of both oxygen stoichiometry and crystallinity. Classical molecular dynamics simulations confirm that the controlled formation of conduction channels arises from confinement of the electric field, yielding a reproducible spatial distribution of oxygen vacancies across switching cycles. Furthermore, this work demonstrates an effective route to control the otherwise random electroforming process by electrode design, facilitating the development of more accurate memristive devices for neuromorphic computing.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Energy Efficient Streaming Time Series Classification with Attentive Power Iteration

Efficiently processing time series data streams in real-time on resource-constrained devices offers significant advantages in terms of enhanced computational energy efficiency and reduced time-related risks. We introduce an innovative streaming time series classification network that utilizes attentive power iteration, enabling real-time processing on resource-constrained devices. Our model continuously updates a compact representation of the entire time series, enhancing classification accuracy while conserving energy and processing time. Notably, it excels in streaming scenarios without requiring complete time series access, enabling swift decisions. Experimental results show that our approach excels in classification accuracy and energy efficiency, with over 70% less consumption and threefold faster task completion than benchmarks. This work advances real-time responsiveness, energy conservation, and operational effectiveness for constrained devices, contributing to optimizing various applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Virtual Neuron: A Neuromorphic Approach for Encoding Numbers

Neuromorphic computers perform computations by emulating the human brain and are expected to be indispensable for energy-efficient computing in the future. They are primarily used in spiking neural network-based machine learning applications. However, neuromorphic computers are unable to preprocess data for these applications. Currently, data is preprocessed on a CPU or a GPU-this incurs a significant cost of transferring data from the CPU/GPU to the neuromorphic processor and vice versa. This cost can be avoided if preprocessing is done on the neuromorphic processor. To efficiently preprocess data on a neuromorphic processor, we first need an efficient mechanism for encoding data that can lend itself to all general-purpose preprocessing operations. Current encoding approaches have limited applicability and may not be suitable for all preprocessing operations. In this paper, we present the virtual neuron as a mechanism for encoding integers and rational numbers on neuromorphic processors. We evaluate the performance of the virtual neuron on physical and simulated neuromorphic hardware and show that it can perform an addition operation using 23 nJ of energy on average using a mixed-signal, memristor-based neuromorphic processor. The virtual neuron encoding approach is the first step in preprocessing data on a neuromorphic processor.

Date, Prasanna↗
DOE OSTI2019

Workshop on Advanced Computing for Connected and Automated Vehicles

To safely and reliably operate without a human driver, connected and automated vehicles (CAVs) require more advanced computing hardware and software solutions than are implemented today in vehicles that provide driver-assistance features. A workshop was held to discuss advanced microelectronics and computing approaches that can help meet future energy and computational requirements for CAVs. Workshop questions were posed as follows: will highly automated vehicles be viable with conventional computing approaches or will they require a step-change in computing; what are the energy requirements to support on-board sensing and computing; and what advanced computing approaches could reduce the energy requirements while meeting their computational requirements? At present, there is no clear convergence in the computing architecture for highly automated vehicles. However, workshop participants generally agreed that there is a need to improve the computing performance per watt by at least 10x to advance the degree of automation. Participants suggested that DOE and the national laboratories could play a near-term role by developing benchmarks for determining and comparing CAV computing performance, developing public data sets to support algorithm and software development, and contributing precompetitive advancements in energy efficient computing.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Best Practices Guide: Benchmarking Energy Efficiency in Laboratories

Purpose and Audience A wide spectrum of laboratory owners, ranging from universities to federal agencies, have explicit goals for energy efficiency and greenhouse gas reductions in their facilities. For example, new federal buildings and major renovations of existing buildings are to reduce fossil fuel-generated energy consumption by 90% in 2025, and 100% in 2030, compared with a 2003 baseline (FEMP n.d.). Minnesota SB2030 standard requires achieving an 80% reduction from the average building baseline for commercial, institutional, and industrial buildings (SB2030 n.d). A laboratory—new or existing—is much more likely to meet energy efficiency goals if quantitative metrics and targets are explicitly specified and tracked over the life cycle of the building, from design through construction, commissioning, operations, and renovations. If efficiency targets are not explicitly and properly defined, any additional capital costs or design time associated with attaining higher efficiencies can be difficult to justify relative to other priorities. The purpose of this guide is to provide guidance on how to specify and compute energy efficiency metrics and benchmarks for laboratories, at the whole-building as well as the system level. The information in this guide can be used to incorporate quantitative metrics and targets into new construction or retrofit of existing facilities. For information on strategies and technologies to achieve energy efficiency, the reader is referred to I2SL resources, including technology best practice guides and case studies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Predictive model using artificial neural network to design phase change material-based ocean thermal energy harvesting systems for powering uncrewed underwater vehicles

Uncrewed Underwater Vehicles (UUVs) are a major beneficiary of the phase change material (PCM)-based ocean thermal energy harvesting technology for their mission needs. However, this technology relies on different parameters and energy conversion steps that could be critical to the general energy generation efficiency. Sea trials showed that the design performed lower than their laboratory design specifications. This underperformance results from different factors, mainly the UUV’s trajectory, travel time, underwater ocean currents, temperature fluctuations, and biofouling on the heat exchanger due to long term underwater operations. Therefore, there exists a need to continuously monitor the ambient energy harvesting system and predict system performance, for mission planning purposes. Two major parameters influencing the energy harvesting system include the final pressure inside the hydraulic energy storage vessel or accumulator, and the electrical load value. Here, this work focuses on the hydraulic to electric energy conversion system. Therefore, a combination of numerical model and experimental testing is used to develop a predictive model using artificial neural network using MATLAB. After validation with experimental testing, 1000 data samples obtained from the numerical model are used to train the ANN. Compared to the experimental results, the developed ANN model can predict in less than a second the designed benchtop system’s total efficiency with less than 15 percent maximum error range. This predictive model development represents a cost-effective way for optimization and a computational energy efficient mode aboard UUVs for mission planning for deployed UUVs using PCM-based ocean thermal energy harvesting technology.

30 DIRECT ENERGY CONVERSION↗