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1. Exploring FPGA Optimizations in OpenCL for Breadth-First Search on Sparse Graph Datasets

   https://doi.org/10.1109/FPL50879.2020.00032  

   Gondhalekar, Atharva; Feng, Wu-Chun (August 2020, 30th International Conference on Field-Programmable Logic and Applications)

   Breath-first search (BFS) is a fundamental building block in many graph-based applications. It is challenging to optimize due to its irregular memory-access pattern. Prior work, based on hardware description languages (HDLs) and high-level synthesis (HLS), address the memory-access bottleneck by using techniques such as edge-centric traversal, data alignment, and compute-unit (CU) replication. While these optimizations work well for dense graph datasets, optimizing BFS on sparse graphs remains a significant challenge due to the kernel launch overhead and poor workload distribution across processing elements. As a complement to the prior work, we present and evaluate optimizations in OpenCL for BFS on sparse graphs. Specifically, we explore application-specific and architecture-aware optimizations aimed at mitigating the irregular global-memory access bottleneck in sparse graphs. In our kernel design, we consider factors such as choice of data structure between queue and array, number of memory banks, and kernel launch configuration. We evaluate the impact of proposed optimizations on a diverse set of sparse graphs. In comparison with the state-of-the-art OpenCL implementation for FPGA, we achieve 5.7x-22.3x speedup on Stratix 10 SX 2800 FPGA for the graphs that are most sensitive to our optimization scheme. 

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2. GAN-DUF: Hierarchical Deep Generative Models for Design Under Free-Form Geometric Uncertainty

   https://doi.org/10.1115/1.4055898  

   Chen, Wei; Lee, Doksoo; Balogun, Oluwaseyi; Chen, Wei (January 2023, Journal of Mechanical Design)

   Abstract Deep generative models have demonstrated effectiveness in learning compact and expressive design representations that significantly improve geometric design optimization. However, these models do not consider the uncertainty introduced by manufacturing or fabrication. The past work that quantifies such uncertainty often makes simplifying assumptions on geometric variations, while the “real-world,” “free-form” uncertainty and its impact on design performance are difficult to quantify due to the high dimensionality. To address this issue, we propose a generative adversarial network-based design under uncertainty framework (GAN-DUF), which contains a deep generative model that simultaneously learns a compact representation of nominal (ideal) designs and the conditional distribution of fabricated designs given any nominal design. This opens up new possibilities of (1) building a universal uncertainty quantification model compatible with both shape and topological designs, (2) modeling free-form geometric uncertainties without the need to make any assumptions on the distribution of geometric variability, and (3) allowing fast prediction of uncertainties for new nominal designs. We can combine the proposed deep generative model with robust design optimization or reliability-based design optimization for design under uncertainty. We demonstrated the framework on two real-world engineering design examples and showed its capability of finding the solution that possesses better performance after fabrication. 

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3. Hierarchical Deep Generative Models for Design Under Free-Form Geometric Uncertainty

   https://doi.org/10.1115/DETC2022-89707  

   Chen, Wei; Lee, Doksoo; Balogun, Oluwaseyi; Chen, Wei (August 2022, American Society of Mechanical Engineers)

   Abstract Deep generative models have demonstrated effectiveness in learning compact and expressive design representations that significantly improve geometric design optimization. However, these models do not consider the uncertainty introduced by manufacturing or fabrication. Past work that quantifies such uncertainty often makes simplifying assumptions on geometric variations, while the “real-world”, “free-form” uncertainty and its impact on design performance are difficult to quantify due to the high dimensionality. To address this issue, we propose a Generative Adversarial Network-based Design under Uncertainty Framework (GAN-DUF), which contains a deep generative model that simultaneously learns a compact representation of nominal (ideal) designs and the conditional distribution of fabricated designs given any nominal design. This opens up new possibilities of 1) building a universal uncertainty quantification model compatible with both shape and topological designs, 2) modeling free-form geometric uncertainties without the need to make any assumptions on the distribution of geometric variability, and 3) allowing fast prediction of uncertainties for new nominal designs. We can combine the proposed deep generative model with robust design optimization or reliability-based design optimization for design under uncertainty. We demonstrated the framework on two real-world engineering design examples and showed its capability of finding the solution that possesses better performances after fabrication. 

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4. Bayesian Sequential Design Based on Dual Objectives for Accelerated Life Tests

   https://doi.org/10.1007/978-3-030-20709-0_11  

   Lu, L; Lee, I; Hong, Y (January 2019, Statistical Quality Technologies)

   Traditional accelerated life test plans are typically based on optimizing the C-optimality for minimizing the variance of an interested quantile of the lifetime distribution. These methods often rely on some specified planning values for the model parameters, which are usually unknown prior to the actual tests. The ambiguity of the specified parameters can lead to suboptimal designs for optimizing the reliability performance of interest. In this paper, we propose a sequential design strategy for life test plans based on considering dual objectives. In the early stage of the sequential experiment, we suggest allocating more design locations based on optimizing the D-optimality to quickly gain precision in the estimated model parameters. In the later stage of the experiment, we can allocate more observations based on optimizing the C-optimality to maximize the precision of the estimated quantile of the lifetime distribution. We compare the proposed sequential design strategy with existing test plans considering only a single criterion and illustrate the new method with an example on the fatigue testing of polymer composites. 

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5. E-Graphs as Circuits, and Optimal Extraction via Treewidth

   Sun, Glenn; Zhang, Yihong; Ni, Haobin (November 2024, arXivorg)

   We demonstrate a new connection between e-graphs and Boolean circuits. This allows us to adapt existing literature on circuits to easily arrive at an algorithm for optimal e-graph extraction, parameterized by treewidth, which runs in 2^{O(w^2)} poly(w, n) time, where w is the treewidth of the e-graph. Additionally, we show how the circuit view of e-graphs allows us to apply powerful simplification techniques, and we analyze a dataset of e-graphs to show that these techniques can reduce e-graph size and treewidth by 40-80% in many cases. While the core parameterized algorithm may be adapted to work directly on e-graphs, the primary value of the circuit view is in allowing the transfer of ideas from the well-established field of circuits to e-graphs. 

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