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DOE OSTI2024

Kilonova light-curve interpolation with neural networks

Kilonovae are the electromagnetic transients created by the radioactive decay of freshly synthesized elements in the environment surrounding a neutron star merger. To study the fundamental physics in these complex environments, kilonova modeling requires, in part, the use of radiative transfer simulations. The microphysics involved in these simulations results in high computational cost, prompting the use of emulators for parameter inference applications. Utilizing a training set of 22 248 high-fidelity simulations (composed of 412 unique ejecta parameter combinations evaluated at 54 viewing angles), we use a neural network to efficiently train on existing radiative transfer simulations and predict light curves for new parameters in a fast and computationally efficient manner. Our neural network can generate millions of new light curves in under a minute. We discuss our emulator's degree of off-sample reliability and parameter inference of the AT2017gfo observational data. Finally, we discuss tension introduced by multiband inference in the parameter inference results, particularly with regard to the neural network's recovery of viewing angle. Published by the American Physical Society 2024

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Telecom Networking with a Diamond Quantum Memory

Practical quantum networks require interfacing quantum memories with existing channels and systems that operate in the telecom band. Here we demonstrate low-noise, bidirectional quantum frequency conversion that enables a solid-state quantum memory to directly interface with telecom-band systems. In particular, we demonstrate conversion of visible-band single photons emitted from a silicon-vacancy ( Si V ) center in diamond to the telecom O band, maintaining low noise ( g 2 ( 0 ) < 0.1 ) and high indistinguishability ( V = 89 ± 8 % ). We further demonstrate the utility of this system for quantum networking by converting telecom-band time-bin pulses, sent across a lossy and noisy 50-km deployed fiber link, to the visible band and entangling them with a diamond quantum memory with fidelity F ≥ 87 ± 2.5 % . These results demonstrate the viability of Si V quantum memories integrated with telecom-band systems for scalable quantum networking applications. Published by the American Physical Society 2024

Physics↗

DOE OSTI2025

Performance Evaluation of a Novel Sequence-Based Directional Detection Strategy for Protection of Active Distribution Networks

Directional elements are relied on to achieve selectivity in fault detection in power systems. Although such elements have been deployed successfully for many years, there is an increased need for novel methods to deal with the unique challenges of directional protection in modern distribution networks. This article analyzes the impact of inverter-based resources (IBRs) on existing directional protection methods in distribution systems. It identifies parts of such elements that pose a risk of misoperation when IBRs are used in distribution networks. The authors have developed a new directional detection method for unbalanced faults in such networks using superimposed symmetrical sequence quantities. The phase angle of the superimposed negative sequence admittance is used to determine fault direction. The paper also presents a real-time co-simulation platform between a simulated distribution system and physical protection relay, using OPAL-RT. An SEL-411L relay is used to program the detection algorithm. This hardware-in-the-loop (HIL) setup is used to verify the performance of the method and the results are compared with existing directional methods

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Experimental Validation of a 40kW, 480V Point-to-Point DC Interlinks for Controller-Agnostic, Interoperable Networked Microgrids

This paper presents the experimental validation of point-to-point dc-interlinks for interconnecting two solar-based, laboratory-scale AC microgrids. DC interlinks provide a solution to numerous technical and operational challenges encountered in networked microgrids, including precise power flow control, stable and fast synchronization, enhanced stability, and improved voltage and frequency regulation. By decoupling microgrids through power converters, dc-interlinks enable power exchange among microgrids that can be owned by different entities (such as communities, utilities, or universities) and managed by diverse microgrid controller vendors. This characteristic makes this dc-interlinks a promising solution for networking real-world microgrids. The presented point-to-point dc interlink utilizes two four-quadrant converters: a 40kW 3-phase ac/dc regulating the dc-link voltage to 800V, and a 3-phase dc/ac controlling power flow. These converters, connected via a 20-foot dc cable, interconnect two ac microgrids operating at 480V, each featuring energy storage, photovoltaic generation, and load emulation. Experimental validation employs commercially available off-the-shelf (COTS) converters and real-world data from solar-powered microgrids in Adjuntas, Puerto Rico. To the authors’ knowledge, this work provides the first at-scale experimental validation of dc-interlinks for networked ac microgrids using COTS inverters, demonstrating their practicality and effectiveness in addressing real-world operational challenges.

Ferrari Maglia, Max [ORNL]↗

DOE OSTI2021

A Knowledge Network-Based Approach to Facilitate Annotation of Clinical Pathway Component Clusters

Mining electronic health records (EHRs) to identify contextually related clinical concept clusters that tend to co-occur temporarily and consistently could improve data-driven clinical pathway (CP) construction. However, the automatic extraction of contextually related clinical concept clusters contains a vast amount of irrelevant information. Hence, this paper proposes a knowledge network-enabled literature-based discovery (LBD) approach to remove noise from clusters. The authors used published literature to filter spurious concepts from the clusters and used data from the US Department of Veterans Affairs’s major depressive disorder (MDD) cohort of Operation Enduring Freedom/Operation Iraqi Freedom (OEF/OIF) for their experimentation. The approach was applied to 2,967 clusters extracted from the MDD OEF/OIF database. The experimental results demonstrate that the proposed approach can filter 94% of the irrelevant information. Moreover, the authors applied various network mining algorithms to analyze the clusters and demonstrated that LBD, along with network mining techniques, is a useful method for finding accurate contextually related clinical concept clusters. This could help domain researchers perform advanced analytics in CPs.

Hasan, S M Shamimul↗

DOE OSTI2022

A Q-Learning-Based Approach for Distributed Beam Scheduling in mmWave Networks

We consider the problem of distributed downlink beam scheduling and power allocation for millimeter-Wave (mmWave) cellular networks where multiple base stations (BSs) belonging to different service operators share the same unlicensed spectrum with no centralized control or explicit coordination among them. Our goal is to design efficient distributed beam scheduling and power allocation schemes such that the network-level payoff, defined as the weighted sum of the total throughput and a power penalization term, can be maximized. To this end, we propose a distributed scheduling approach to power allocation and adaptation for efficient interference management over the shared spectrum by modeling each BS as an independent Q-learning agent. As a baseline, we compare the proposed approach to the state-of-the-art non-cooperative game-based approach which was previously developed for the same problem. We conduct extensive experiments under various scenarios to verify the effect of multiple factors on the performance of both approaches. Experiment results show that the proposed approach adapts well to different interference conditions by learning from experience and thus can achieve higher payoff than the game-based approach. The proposed approach can also be integrated into our previously developed Lyapunov stochastic optimization framework for the purpose of network utility maximization with optimality guarantee. As a result, the weights in the payoff function can be automatically and optimally determined by the virtual queue values from the sub-problems derived from the Lyapunov optimization framework.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

A Parallel Alternative for Energy-Efficient Neural Network Training and Inferencing

Energy efficiency of training and inferencing with large neural network models is a critical challenge facing the future of sustainable large-scale machine learning workloads. This paper introduces an alternative strategy, called phantom parallelism, to minimize the net energy consumption of traditional tensor (model) parallelism, the most energy-inefficient component of large neural network training. The approach is presented in the context of feed-forward network architectures as a preliminary, but comprehensive, proof-of-principle study of the proposed methodology. We derive new forward and backward propagation operators for phantom parallelism, implement them as custom autograd operations within an end-to-end phantom parallel training pipeline and compare its parallel performance and energy-efficiency against those of conventional tensor parallel training pipelines. Formal analyses that predict lower bandwidth and FLOP counts are presented with supporting empirical results on up to 256 GPUs that corroborate these gains. Experiments are shown to deliver ∼50% reduction in the energy consumed to train FFNs using the proposed phantom parallel approach when compared with conventional tensor parallel methods. Additionally, the proposed approach is shown to train smaller phantom models to the same model loss on smaller GPU counts as larger tensor parallel models on larger GPU counts offering the possibility for even greater energy savings.

Seal, Sudip [ORNL] (ORCID:0000000332330656)↗

DOE OSTI2022

Disruption-Robust Community Detection Using Consensus Clustering in Complex Networks

Topological (graph-theoretic) analysis of critical infrastructure networks provides insight on several aspects of resilience. Graph clustering or community detection, which identifies densely connected components in a graph, has been employed for analysis. In this paper, we propose employing consensus clustering, which is a technique to determine consensus from a collection of different clusters on an input, such that the resulting clustering is robust to disruptions, where a disruption is represented as loss of one or more vertices or edges in the graph. Using two critical infrastructure networks as case studies, we empirically demonstrate the need to compute consensus clustering in order to address the drastic changes in the topology due to disruptions in the network.

Hussain, Md Taufique↗

DOE OSTI2024

Blockchain-Enabled Secure Device-to-Device Communication in Software-Defined Networking

The Internet of Things (IoT) continues to increase the demand for seamless communication among IoT devices. The rapid growth of IoT devices has led to an exponential increase in device-to-device (D2D) communication within the Software-Defined Networking (SDN), though it enables a flexible archi-tecture for managing network resources. However, traditional security models face challenges (e.g., Security, privacy, and trust) in addressing the dynamic and decentralized nature of these communications. Despite of these challenges, this paper proposes a novel approach that leverages blockchain technology to enhance the security, privacy, and trustworthiness of D2D communication within an SDN environment. The proposed approach integrates blockchain nodes in sDN components to establish a decentralized ledger for transparent and verifiable records. Smart contracts enforce authentication rules to ensure that only authenticated devices can access the network and engage in transactions securely. It also automates the security policies to ensure temper resistance execution using the cryptographic mechanism for data integrity and authentic communication. The Implementation of the proposed algorithms validates the resilience of the proposed approach against cyberattacks. Overall, the proposed approach enables efficient and secure D2D communication for resilient SDN infrastructure in IoT ecosystems.

Das, Debashis↗

DOE OSTI2022

Neuromorphic Accelerator for Deep Spiking Neural Networks with NVM Crossbar Arrays

In this paper, we present a scalable digital hardware accelerator based on non-volatile memory arrays capable of realizing deep convolutional spiking neural networks (SNNs). Our design studies are conducted using a compact model for spin-transfer torque random access memory (STT-RAM) devices. Large networks are realized by tiling multiple cores which communicate by transmitting spike packets via an on-chip routing network. Compared to an equivalent SRAM based core design, we show that the STT-RAM based design achieves nearly 15X higher GSOPS (Synaptic Operations per Second) per Watt per mm 2 making it a promising platform for realizing systems with significant area and power limitations.

Kulkarni, Shruti↗

DOE OSTI2023

Hyperparameter Optimization and Feature Inclusion in Graph Neural Networks for Spiking Implementation

Graph convolutional networks leverage both graph structures and features on nodes and edges for improved learning performance in comparison with classical machine learning approaches. Spiking neuromorphic computers natively implement network-like computation and have been shown to be successful at implementing graph learning without features. Incorporating graph features brings the challenge of efficient feature representation and balancing the contribution of topology and features in learning. In this work, we present our design of a simulated network of spiking neurons to perform semi-supervised learning on graph data using both the graph structure and the node features. We explore various design choices, present preliminary results, and discuss the opportunities for using neuromorphic computers for this task in the future.

Cong, Guojing↗

DOE OSTI2021

A Non-cooperative Game-based Approach to Distributed Beam Scheduling in Millimeter-Wave Networks

We consider the distributed beam scheduling problem in mm-Wave networks where the base stations may belong to different operators and there is no centralized coordination among them. Our goal is to design distributed beam scheduling algorithms such that the network utility, which is defined as a logarithm function of the average throughput of the user equipment, can be maximized. We propose a non-cooperative game-based scheduling approach where the base stations are modeled as players that greedily maximize their own utilities. The Nash Equilibrium (NE) then provides a distributed solution to the network utility maximization problem. By employing the Lyapunov optimization, the asymptotic optimality of the proposed scheduling can be guaranteed. We prove the existence and provide sufficient conditions which guarantee the uniqueness of the NE by establishing an equivalence to the Variational Inequality (VI) problem. We also propose a parallel power adaptation algorithm which is proved to converge to the NE. Numerical results show the superiority of the proposed scheduling over several distributed baseline schemes.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2024

Coordinated Self-Assembly of Networked Microgrids Using Irving’s Algorithm

Abstract— Microgrid installations have regained significant interest, driven by the increasing adoption of distributed energy resources, decentralized controls, and ongoing technological advancements. While the number of microgrids increases, there are opportunities to coordinate networks of microgrids. In this study, Irving’s algorithm is applied to a framework for the coordinated self-assembly of networked microgrids. Compared with the authors’ previous work, this new study relaxes the constraint on participated microgrids to have a single global objective. Thus, each microgrid can rank others according to its local preference. This reduces the amount of shared information, improves the privacy, and enhances the flexibility. The adaptability to that proposed framework is also maintained. Examples are presented to illustrate the networking processes and self-assembly operations.

Xie, Jing↗

DOE OSTI2024

Automatic Extraction of Network Configurations for Realistic Simulation and Validation

Popular HPC network interconnection simulators such as SST Macro provide a variety of configurable parameters to explore the design space of hardware components such as network links and switches. While such knobs provide flexibility to explore design trade-offs for novel hardware, manually configuring simulations for existing hardware to focus on topology exploration can be cumbersome and error-prone, leading to widely inaccurate simulations. This challenge is compounded when specifications of various (proprietary) technologies are not readily available or are intentionally omitted. In this work, we provide a methodology to automatically tune the simulation configuration of the multiple network models running within SST Macro using Bayesian optimization. We perform this optimization in the context of multiple messaging regimes (i.e., small to large and latency to bandwidth-bound messages) and provide a detailed analysis of the simulation error for four systems. With our automated framework, we achieve a 5x improvement in accuracy over best-effort configurations based on available hardware specifications.

Suetterlein, Joshua D.↗

DOE OSTI2025

Anomaly Detection in Electronic Health Records Across Hospital Networks: Integrating Machine Learning With Graph Algorithms

In a large hospital system, a network of hospitals relies on electronic health records (EHRs) to make informed decisions regarding their patients in various clinical domains. Consequently, the dependability of the health information technology (HIT) systems responsible for collecting EHR data is of utmost importance for patient safety. Recently, novel methods and tools aimed at identifying anomalies in EHR data to bolster the reliability of HIT systems have been introduced. However, these existing methods and tools primarily concentrate on individual hospitals, which limits our understanding of system-wide anomalous events and their potential impact on patient safety across multiple hospitals. In this article, we introduce a new approach to detecting anomalies in EHR data within a network of hospitals. This is achieved by combining advanced machine learning techniques with graph algorithms to create a tool capable of swiftly identifying and responding to deviations. Our proposed approach employs a combination of five machine learning models, harnessing the unique strengths of each model to provide a more robust detection system. The detected anomalies are then represented as graphs, allowing us to recognize patterns across the hospital network. This aids in identifying anomalies that span multiple medical facilities, potentially indicating broader system-level risks. Extensive real-world testing of our approach demonstrated its ability to offer actionable insights compared to existing methods. Additionally, its scalable design ensures seamless integration into existing HIT infrastructures.

Niu, Haoran [Oak Ridge National Laboratory (ORNL),↗

DOE OSTI2020

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing

The recent development of deep learning has been mostly focusing on Euclidean data, such as images, videos, audios, etc. However, most real-world information and relation are often expressed as graphs. To efficiently learn from graph data, graph convolutional networks (GCNs) emerge as a promising approach, showing advantages in several practical applications such as social network analysis, knowledge discovery, 3D modeling, motion capturing, etc. Real-world graphs are usually extremely large and imbalanced, posting significant performance demand and design challenges on the hardware dedicated for GCN inference. In this paper, we propose an architecture design called UW-GCN to accelerate graph convolutional network inference. To tackle the major performance bottleneck from workload imbalance, we propose dynamic neighborhood stealing and remote chunk shuffling techniques, relying on hardware flexibility to achieve hardware auto-tuning under negligible area or delay overhead. Specifically, UW-GCN is able to smartly profile the sparse graph pattern while continuously adjusting the workload distribution via routing reconfiguration among parallel processing elements (PEs). The ideal configuration is then reused in the remaining iterations. To the best of our knowledge, this is the first accelerator design particularly for GCN and the first work relying on hardware auto-tuning, which is normally based on software, to achieve near-optimal workload balance in processing sparse structures.

Geng, Tong↗

DOE OSTI2021

Event Cause Analysis in Distribution Networks using Synchro Waveform Measurements

This paper presents a machine learning method for event cause analysis to enhance situational awareness in distribution networks. The data streams are captured using time-synchronized high sampling rates synchro waveform measurement units (SWMU). The proposed method is formulated based on a machine learning method, the convolutional neural network (CNN). This method is capable of capturing the spatiotemporal feature of the measurements effectively and perform the event cause analysis. Several events are considered in this paper to encompass a range of possible events in real distribution networks, including capacitor bank switching, transformer energization, fault, and high impedance fault (HIF). The dataset for our study is generated using the real time digital simulator (RTDS) to simulate real-world events. The event cause analysis is performed using only one cycle of the voltage waveforms after the event is detected. The simulation results show the effectiveness of the proposed machine learning-based method compared to the state-of-the-art classifiers.

Niazazari, Iman↗

DOE OSTI2024

Networked Microgrid Ownership, Data, and Control Implications: Challenges and Open Questions

Microgrid deployments increasingly favor the potential to form networks for greater benefits to resilience, reliability, and energy sovereignty. Both independent and networked micro-grids predominantly have a single-entity-ownership and control, where the associations from ownership to data requirements to control functions to microgrid objectives is linear. The emerging model, however, is cyclical, with bidirectional causal impacts between each of the 4 pillars: there are more complex mixed ownership models across the physical, electrical, data, communications, protection, and control boundaries that impact the data requirements for meeting control functions that help realize the use-cases or objectives. This paper is the first to delineate the pillars for effective ownership and controllability of both independent as well as networked microgrids through the cyclical model, and present barriers to the adoption of such a model.

Sundararajan, Aditya↗