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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. Finding Interior Optimum of Black-box Constrained Objective with Bayesian Optimization

   Zhang, F; Chen, Y (July 2025, The 41st Conference on Uncertainty in Artificial Intelligence)

   Optimizing objectives under constraints, where both the objectives and constraints are black box functions, is a common scenario in real-world applications such as the design of medical therapies, industrial process optimization, and hyperparameter optimization. One popular approach to handle these complex scenarios is Bayesian Optimization (BO). However, when it comes to the theoretical understanding of constrained Bayesian optimization (CBO), the existing framework often relies on heuristics, approximations, or relaxation of objectives and, therefore, lacks the same level of theoretical guarantees as in canonical BO. In this paper, we exclude the boundary candidates that could be compromised by noise perturbation and aim to find the interior optimum of the black-box-constrained objective. We rely on the insight that optimizing the objective and learning the constraints can both help identify the high-confidence regions of interest (ROI) that potentially contain the interior optimum. We propose an efficient CBO framework that intersects the ROIs identified from each aspect on a discretized search space to determine the general ROI. Then, on the ROI, we optimize the acquisition functions, balancing the learning of the constraints and the optimization of the objective. We showcase the efficiency and robustness of our proposed CBO framework through the high probability regret bounds for the algorithm and extensive empirical evidence. 

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