Riaz-Ul-Haque Mian

Wakuto Matsumi, Riaz-Ul-Haque Mian

Machine learning based on neural networks has advanced rapidly, but the high energy consumption required for training and inference remains a major challenge. Hyperdimensional Computing (HDC) offers a lightweight, brain-inspired alternative that enables high parallelism but often suffers from lower accuracy on complex visual tasks. To overcome this, hybrid accelerators combining HDC and Convolutional Neural Networks (CNNs) have been proposed, though their adoption is limited by poor generalizability and programmability. The rise of open-source RISC-V architectures has created new opportunities for domain-specific GPU design. Unlike traditional proprietary GPUs, emerging RISC-V-based GPUs provide flexible, programmable platforms suitable for custom computation models such as HDC. In this study, we design and implement custom GPU instructions optimized for HDC operations, enabling efficient processing for hybrid HDC-CNN workloads. Experimental results using four types of custom HDC instructions show a performance improvement of up to 56.2 times in microbenchmark tests, demonstrating the potential of RISC-V GPUs for energy-efficient, high-performance computing.

Wang WeiQuan, Riaz-ul-Haque Mian

In semiconductor manufacturing, testing costs remain significantly high, especially during wafer and FPGA testing. To reduce the number of required tests while maintaining predictive accuracy, this study investigates three baseline sampling strategies: Random Sampling, Stratified Sampling, and k-means Clustering Sampling. To further enhance these methods, this study proposes a novel algorithm that improves the sampling quality of each approach. This research is conducted using real industrial production data from wafer-level tests and silicon measurements from various FPGAs. This study introduces two hybrid strategies: Stratified with Short Distance Elimination (S-SDE) and k-means with Short Distance Elimination (K-SDE). Their performance is evaluated within the framework of Gaussian Process Regression (GPR) for predicting wafer and FPGA test data. At the core of our proposed approach is the Short Distance Elimination (SDE) algorithm, which excludes spatially proximate candidate points during sampling, thereby ensuring a more uniform distribution of training data across the physical domain. A parameter sweep was conducted over the (alpha, beta) thresholds, where alpha and beta are in the range {0, 1, 2, 3, 4} and not both zero, to identify the optimal combination that minimizes RMSD. Experimental results on a randomly selected wafer file reveal that (alpha, beta) equal (2, 2) yields the lowest RMSD. Accordingly, all subsequent experiments adopt this parameter configuration. The results demonstrate that the proposed SDE-based strategies enhance predictive accuracy: K-SDE improves upon k-means sampling by 16.26 percent (wafer) and 13.07 percent (FPGA), while S-SDE improves upon stratified sampling by 16.49 percent (wafer) and 8.84 percent (FPGA).

Aliffi Majiid, Riaz-Ul-Haque Mian, Kouki Kurohara et al.

Revitalizing Japan's remote areas has become a crucial task, and Matsue City exemplifies this effort in its temporary event spaces, created through collective efforts to foster urban vibrancy and bring together residents and visitors. This research examines the relationship between data-driven in-sights using generative AI and visual attractiveness by evaluating tempo-rary events in Matsue City, particularly considering the cognitive-cultural differences in processing visual information of the participants. The first phase employs semantic keyword extraction from interviews, categorizing responses into physical elements, activities, and atmosphere. The second phase analyzes spatial perception through three categories: layout hierar-chy, product visibility, and visual attention. The correlation indicates that successful event design requires a balance between spatial efficiency and diverse needs, with a spatial organization that optimizes visitor flow and visibility strategies considering cultural and demographic diversity. These findings contribute to understanding the urban quality of temporary event spaces and offer a replicable framework for enhancing the visual appeal of events in remote areas throughout Japan.