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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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DOE OSTI2020

SODA: a New Synthesis Infrastructure for Agile Hardware Design of Machine Learning Accelerators

Next generation systems, such as edge devices, will have to provide efficient processing of machine learning (ML) algorithms along several metrics, including energy, performance, area, and latency. However, the quickly evolving field of ML makes it extremely difficult to generate accelerators able to support a wide variety of algorithms. At the same time, designing accelerators in hardware description languages (HDLs) by hand is hard and time consuming, and does not allow quick exploration of the design space. This paper discusses the SODA synthesizer, an automated open source high-level ML framework-to-Verilog compiler targeting ML Application-Specific Integrated Circuits (ASICs) chiplets based on the LLVM infrastructure. The SODA synthesizers will allow implementing optimal designs by combining templated and fully tunable IPs and macros, and fully custom components generated through high-level synthesis. All these components will be provided through an extendable resource library, characterized with both commercial and open source logic design flows. Through a closed loop design space exploration engine, developers will be able to quickly explore their hardware designs along different dimension

Minutoli, Marco↗

DOE OSTI2020

Scalable Deep-Learning-Accelerated Topology Optimization for Additively Manufactured Materials

Topology optimization (TO) is a popular and powerful computational approach for designing novel structures, materials, and devices. Two computational challenges have limited the applicability of TO to a variety of industrial applications. First, a TO problem often involves a large number of design variables to guarantee sufficient expressive power. Second, many TO problems require a large number of expensive physical model simulations, and those simulations cannot be parallelized. To address these issues, we propose a general scalable deep-learning (DL) based TO framework, referred to as SDL-TO, which utilizes parallel schemes in high performance computing (HPC) to accelerate the TO process for designing additively manufactured (AM) materials. Unlike the existing studies of DL for TO, our framework accelerates TO by learning the iterative history data and simultaneously training on the mapping between the given design and its gradient. The surrogate gradient is learned by utilizing parallel computing on multiple CPUs incorporated with a distributed DL training on multiple GPUs. The learned TO gradient enables a fast online update scheme instead of an expensive update based on the physical simulator or solver. Using a local sampling strategy, we achieve to reduce the intrinsic high dimensionality of the design space and improve the training accuracy and the scalability of the SDL-TO framework. The method is demonstrated by benchmark examples and AM materials design for heat conduction. The proposed SDL-TO framework shows competitive performance compared to the baseline methods but significantly reduces the computational cost by a speed up of around 8.6x over the standard TO implementation.

Bi, Sirui↗

DOE OSTI2021

Field Accelerated Stress Testing (FAST): Forecasting Plant-specific Failure Modes and Degradation Rates

Photovoltaic plant owners and insurance companies would like to be prepared for the forthcoming material warranty claims (material defects) and power warranty claims (degradation rate). To address the needs of these stakeholders, this presentation aims to introduce a new conceptual approach to perform accelerated testing right at the field of interest and it is called field accelerated stress testing (FAST). The primary goal of this presentation is to present a conceptual approach to forecast the field specific failure modes and degradation rates in advance by a factor of 2 to 5, right at the plant site using a few spare modules available at the plant location itself.

14 SOLAR ENERGY↗

DOE OSTI2021

Binary Complex Neural Network Acceleration on FPGA

Being able to learn from complex data with phase information is imperative for many signal processing applications. Today’s real-valued deep neural networks (DNNs) have shown efficiency in latent information analysis but fall short when applied to the complex domain. Deep complex networks (DCN) , in contrast, can learn from complex data, but have high computational costs; therefore, they cannot satisfy the instant decision making requirements of many deployable systems dealing with short observations or short signal bursts. Recent, Binarized Complex Neural Network (BCNN), which integrates DCNs with binarized neural networks (BNN), shows great potential in classifying complex data in real-time. In this paper, we propose a structural pruning based accelerator of BCNN, which is able to provide more than 5000 frames/s inference throughput on edge devices. The high performance comes from both the algorithm and hardware sides. On the algorithm side, we conduct structural pruning to the original BCNN models and obtain 20 × pruning rates with negligible accuracy loss; on the hardware side, we propose a novel 2D convolution operation accelerator for the binary complex neural network. Experimental results show that the proposed design works with over 90% utilization and is able to achieve the inference throughput of 5882 frames/s and 4938 frames/s for complex NIN-Net and ResNet-18 using CIFAR-10 dataset and Alveo U280 Board.

Peng, Hongwu↗

DOE OSTI2020

thornado-transport: Anderson- and GPU-accelerated nonlinear solvers for neutrino-matter coupling

Algorithms for neutrino-matter coupling in core-collapse supernovae (CCSNe) are investigated in the context of a spectral two-moment model, which is discretized in space with the discontinuous Galerkin method, integrated in time with implicit-explicit (IMEX) methods, and implemented in the toolkit for high-order neutrino-radiation hydrodynamics (thornado). The model considers electron neutrinos and antineutrinos and tabulated opacities from Bruenn (1985), which includes neutrino-electron scattering and pair processes. The nonlinear system arising from implicit time discretization of the equations governing neutrino-matter coupling is iterated to convergence using Anderson-accelerated fixed-point methods, which avoid formation of Jacobians and inversion of dense linear systems. Numerical experiments show that, for a given tolerance, a nested iteration scheme which aims to reduce opacity evaluations can lower the computational cost. Our initial port to GPUs, using both OpenMP and OpenACC, shows an overall speedup of up to ~ 100× when compared to results using a single CPU core. These results indicate that the algorithms implemented in thornado are well-suited to GPU acceleration.

Laiu, Paul↗

DOE OSTI2021

Data transfer acceleration

Data transfer acceleration includes receiving, by a data transfer accelerator in a first node of a plurality of nodes, from a second node of the plurality of nodes, a request for data in a second state, wherein the second node stores an instance of the data in a first state; generating a message including one or more operations to transform the data from the first state to the second state; and sending the message to the second node in response to the request.

Gutierrez, Anthony↗

DOE OSTI2021

High Power Superconducting Electron Linear Accelerators For Industrial Application

Fermilab is executing a technology development program to develop a compact yet powerful electron accelerator. We are leveraging R&D breakthroughs in SRF cavities, cost-effective radio-frequency sources, modern cryo-coolers, and high average current electron guns. We show that a single accelerator module can deliver average beam power as high as 1 MW through detailed thermal, RF, and particle simulations.

Thangaraj, Jayakar Tobin↗

DOE OSTI2022

EMPOWER: Accelerating the Transformation of the Global Energy Economy - 2022 JISEA Annual Report

The 2022 JISEA Annual Report describes accomplishments from the past year at the Joint Institute for Strategic Energy Analysis, which is accelerating the transformation of the global energy economy. 2021 was filled collaboration and high-impact multidisciplinary projects, exploring everything from the energy-water-food nexus, to the cobalt supply chain, to sustainable communities. This year also welcomed the JISEA Catalyzers Initiative that is accelerating research capabilities at the National Renewable Energy Laboratory. The Catalyzers are empowering communities and research partners to by bringing together experts working within one space to discuss capabilities and future needs to continue to stay ahead of the research curve.

clean energy↗

DOE OSTI2022

Anderson Acceleration for Distributed Training of Deep Learning Models

Anderson acceleration (AA) is an extrapolation technique that has recently gained interest in the deep learning (DL) community to speed-up the sequential training of DL models. However, when performed at large scale, the DL training is exposed to a higher risk of getting trapped into steep local minima of the training loss function, and standard AA does not provide sufficient acceleration to escape from these steep local minima. This results in poor generalizability and makes AA ineffective. To restore AA’s advantage to speed-up the training of DL models on large scale computing platforms, we combine AA with an adaptive moving average procedure that boosts the training to escape from steep local minima. By monitoring the relative standard deviation between consecutive iterations, we also introduce a criterion to automatically assess whether the moving average is needed. We applied the method to the following DL instantiations for image classification: (i) ResNet50 trained on the open-source CIFAR100 dataset and (ii) ResNet50 trained on the open-source ImageNet1k dataset. Numerical results obtained using up to 1,536 NVIDIA V100 GPUs on the OLCF supercomputer Summit showed the stabilizing effect of the moving average on AA for all the problems above.

Lupo Pasini, Massimiliano↗

DOE OSTI2022

Net Zero World Initiative: Accelerating Global Energy System Decarbonization

The United States, partner countries and philanthropies are joining forces to accelerate the transition to clean, secure energy systems and build a Net Zero World. The Net Zero World Initiative leverages expertise across U.S. government agencies and Department of Energy (DOE) national laboratories, in partnership with other governments and philanthropies, to accelerate the decarbonization of global energy systems. This whole-of-government approach supports countries committed to raising their climate ambitions by creating and implementing highly tailored, actionable technical and investment strategies that put net zero within reach. The Net Zero World Initiative enables country partners to harness the power and technical expertise of U.S. and international industry, think tanks, and universities.

clean energy investment strategies↗

DOE OSTI2022

LETID in Legacy and Modern PV Modules: Accelerated Testing and Field Deployment

The kinetics of light- and elevated temperature-induced degradation (LETID) in silicon solar cells depend on the precise operating excess carrier density (?n) of the device. This dependency causes differences in the way LETID manifests in modern, higher-efficiency devices compared to lower-efficiency, legacy devices that might have been deployed in the field in previous years. In this work we model how different vintages of devices are expected to behave in both accelerated laboratory testing, as well as field deployment. The differing excess carrier densities encountered in various module vintages has implications both for interpreting accelerated test data, as well as identifying, diagnosing, and potentially treating LETID in the field.

excess carrier density↗

DOE OSTI2022

FastML Science Benchmarks: Accelerating Real-Time Scientific Edge Machine Learning

Applications of machine learning (ML) are growing by the day for many unique and challenging scientific applications. However, a crucial challenge facing these applications is their need for ultra low-latency and on-detector ML capabilities. Given the slowdown in Moore's law and Dennard scaling, coupled with the rapid advances in scientific instrumentation that is resulting in growing data rates, there is a need for ultra-fast ML at the extreme edge. Fast ML at the edge is essential for reducing and filtering scientific data in real-time to accelerate science experimentation and enable more profound insights. To accelerate real-time scientific edge ML hardware and software solutions, we need well-constrained benchmark tasks with enough specifications to be generically applicable and accessible. These benchmarks can guide the design of future edge ML hardware for scientific applications capable of meeting the nanosecond and microsecond level latency requirements. To this end, we present an initial set of scientific ML benchmarks, covering a variety of ML and embedded system techniques.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

SRF e-beam accelerator for metal additive manufacturing

A system and apparatus for electron beam melting comprises a superconducting radio frequency accelerator configured to produce an electron beam, a conduction cooling system configured to cool the superconducting radio frequency accelerator, and an electron beam melting system wherein the electron beam melts power in a build chamber of the electron beam melting apparatus.

Thangaraj, Jayakar C.↗

DOE OSTI2022

Adaptive time scaling for accelerating explicit finite element analysis

A method for accelerating an explicit finite element analysis (FEA) simulation of a modeled system or process includes performing an initial iteration of the FEA simulation according to a baseline time interval via an FEA computing network, and calculating a criteria ratio of a predetermined set of scaling criteria for the modeled system or process. The method includes determining a time-scaling factor using the criteria ratio via the FEA computing network as a function of the criteria ratio, and then applying the time-scaling factor to the baseline time interval to generate a scaled time interval. The scaled time interval accelerates simulation time of the FEA simulation. The method includes performing a subsequent iteration of the explicit FEA simulation at the scaled time interval using the FEA computing network. The process continues for subsequent iterations, with the time-scaling factor adapting with each iteration.

Chen, Jian↗

DOE OSTI2022

Computational Workflow for Accelerated Molecular Design Using Quantum Chemical Simulations and Deep Learning Models

Efficient methods for searching the chemical space of molecular compounds are needed to automate and accelerate the design of new functional molecules such as pharmaceuticals. Given the high cost in both resources and time for experimental efforts, computational approaches play a key role in guiding the selection of promising molecules for further investigation. Here, we construct a workflow to accelerate design by combining approximate quantum chemical methods [i.e. density-functional tight-binding (DFTB)], a graph convolutional neural network (GCNN) surrogate model for chemical property prediction, and a masked language model (MLM) for molecule generation. Property data from the DFTB calculations are used to train the surrogate model; the surrogate model is used to score candidates generated by the MLM. The surrogate reduces computation time by orders of magnitude compared to the DFTB calculations, enabling an increased search of chemical space. Furthermore, the MLM generates a diverse set of chemical modifications based on pre-training from a large compound library. We utilize the workflow to search for near-infrared photoactive molecules by minimizing the predicted HOMO-LUMO gap as the target property. Our results show that the workflow can generate optimized molecules outside of the original training set, which suggests that iterations of the workflow could be useful for searching vast chemical spaces in a wide range of design problems.

Blanchard, Andrew↗

DOE OSTI2023

Accelerating Adoption of Energy-Efficient Technology with the Thermalize Model

The accelerating impacts of climate change destabilize food systems and ecosystems alongside the communities that rely on them. Thus, climate resilience is a health equity issue at both a physical (relating to biological impacts of pollution and environmental degradation) and a cultural level (loss of subsistence lifestyles, languages, etc). With roughly 20% of US energy-related greenhouse gas emissions stemming from heating, cooling, and powering households, accessible energy-efficient home retrofits are a critical element of climate change mitigation strategies. For communities already producing electricity from renewable sources, like Juneau, incentivizing ductless heat pump upgrades and energy efficiency retrofits provides an accessible pathway to transition households away from oil heating and drastically reduce emissions. Thermalize Juneau is a pilot program that will explore a repeatable framework for accelerating the adoption of energy-efficient technology in Alaska communities. This poster will describe Thermalize Juneau's goals and framework, progress through spring 2021, and future plans.

ductless heat pumps↗

DOE OSTI2023

Accelerating Nuclear Fuels and Materials Qualification by Multi-Level Irradiation Experiment Campaign

The advanced reactor technologies feature fuels, coolants, and materials that promise safer operating conditions under normal and accident scenarios. However, the nuclear fuels and materials qualifications require several decades (for example, new reactor fuel qualification from conceptualization requires about 20 years). Therefore, accelerating nuclear fuels and materials qualification is essential, and it can be achieved by combining high through-put materials irradiation and testing, advanced post-irradiation examinations, and Multiphysics modeling. This paper addressed the associated challenges in accelerating nuclear fuels and material qualification for new and advanced reactor designs, which differ based on fuel, coolant, operating conditions, and structural materials. These challenges vary for radiation level, operating conditions (e.g., temperature and pressure), and coolant type (e.g., corrosion environment). In addition, the challenges and limitations in modeling tools, experimental facilities, and licensing guidelines are also discussed, and a general solution path forward is recommended.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2023

Presentation: Accelerating Nuclear Fuels and Materials Qualification by Multi-Level Irradiation Experiment Campaign

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗