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  1. Free, publicly-accessible full text available May 1, 2025
  2. Free, publicly-accessible full text available January 1, 2025
  3. Recent advances in Graph Neural Networks (GNNs) have changed the landscape of modern graph analytics. The complexity of GNN training and the scalability challenges have also sparked interest from the systems community, with efforts to build systems that provide higher efficiency and schemes to reduce costs. However, we observe that many such systems basically reinvent the wheel of much work done in the database world on scalable graph analytics engines. Further, they often tightly couple the scalability treatments of graph data processing with that of GNN training, resulting in entangled complex problems and systems that often do not scale well on one of those axes.

    In this paper, we ask a fundamental question: How far can we push existing systems for scalable graph analytics and deep learning (DL) instead of building custom GNN systems? Are compromises inevitable on scalability and/or runtimes? We propose Lotan, the first scalable and optimized data system for full-batch GNN training withdecoupled scalingthat bridges the hitherto siloed worlds of graph analytics systems and DL systems. Lotan offers a series of technical innovations, including re-imagining GNN training as query plan-like dataflows, execution plan rewriting, optimized data movement between systems, a GNN-centric graph partitioning scheme, and the first known GNN model batching scheme. We prototyped Lotan on top of GraphX and PyTorch. An empirical evaluation using several real-world benchmark GNN workloads reveals a promising nuanced picture: Lotan significantly surpasses the scalability of state-of-the-art custom GNN systems, while often matching or being only slightly behind on time-to-accuracy metrics in some cases. We also show the impact of our system optimizations. Overall, our work shows that the GNN world can indeed benefit from building on top of scalable graph analytics engines. Lotan's new level of scalability can also empower new ML-oriented research on ever-larger graphs and GNNs. 

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    Free, publicly-accessible full text available July 1, 2024
  4. The proliferation of neural networks in safety-critical applications necessitates the development of effective methods to ensure their safety. This letter presents a novel approach for computing the exact backward reachable sets of neural feedback systems with known linear system models based on hybrid zonotopes. It is shown that the input-output relationship imposed by a ReLU-activated neural network can be exactly described by a hybrid zonotope-represented graph set. Based on that, the one-step exact backward reachable set of a neural feedback system is computed as a hybrid zonotope in the closed form. In addition, a necessary and sufficient condition is formulated as a mixed-integer linear program to certify whether the trajectories of a neural feedback system can avoid unsafe regions in finite time. Numerical examples are provided to demonstrate the efficiency of the proposed approach. 
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    Free, publicly-accessible full text available June 26, 2024
  5. Free, publicly-accessible full text available November 15, 2024
  6. GaN-on-GaN vertical diode is a promising device for next-generation power electronics. Its breakdown voltage (BV) is limited by edge termination designs such as guard rings. The design space of guard rings is huge and it is difficult to optimize manually. In this paper, we propose an effective inverse design strategy to co-optimize BV and (V F Q) −1 , where BV, V F , and Q are the breakdown voltage, forward voltage, and reserve capacitive charge of the diode, respectively. Using rapid Technology Computer-Aided-Design (TCAD) simulations, neural network (NN), and Pareto front generation, a GaN-on-GaN diode is optimized within 24 hours. We can obtain structures with 200V higher BV at medium (V F Q) −1 or find a nearly ideal BV structure with 25% higher BV 2 /R on compared to the best randomly generated TCAD data. 
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  7. Free, publicly-accessible full text available August 1, 2024
  8. Abstract Breakdown voltage (BV) is arguably one of the most critical parameters for power devices. While avalanche breakdown is prevailing in silicon and silicon carbide devices, it is lacking in many wide bandgap (WBG) and ultra-wide bandgap (UWBG) devices, such as the gallium nitride high electron mobility transistor and existing UWBG devices, due to the deployment of junction-less device structures or the inherent material challenges of forming p-n junctions. This paper starts with a survey of avalanche and non-avalanche breakdown mechanisms in WBG and UWBG devices, followed by the distinction between the static and dynamic BV. Various BV characterization methods, including the static and pulse I – V sweep, unclamped and clamped inductive switching, as well as continuous overvoltage switching, are comparatively introduced. The device physics behind the time- and frequency-dependent BV as well as the enabling device structures for avalanche breakdown are also discussed. The paper concludes by identifying research gaps for understanding the breakdown of WBG and UWBG power devices. 
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