Browsing by Author "Yu, Chia-Lin"

Now showing 1 - 2 of 2

Domain-Specific Approximation for Object Detection
(2018-01-01)
Chin, Ting-Wu
;
Yu, Chia-Lin
;
Halpern, Matthew
;
Genc, Hasan
;
Tsao, Shiao-Li
There is growing interest in object detection in advanced driver assistance systems and autonomous robots and vehicles. To enable such innovative systems, we need faster object detection. In this article, we investigate the trade-off between accuracy and speed with domain-specific approximations (DSAs) for two state-of-the-art deep learning-based object detection meta-architectures. We study the effectiveness of applying approximation both statically and dynamically to understand their potential and applicability. By conducting experiments on the ImageNet VID dataset, we show that DSA has great potential to improve the speed of the system without deteriorating the accuracy of object detectors. To this end, we present our insights on harvesting DSA and devise a proof-of-concept runtime, AutoFocus, that exploits dynamic DSA.
Efficient and Portable Workgroup Size Tuning
(2020-02-01)
Yu, Chia-Lin
;
Tsao, Shiao-Li
;
資訊工程學系
;
Department of Computer Science
The performance of an OpenCL program is strongly influenced by both hardware and software attributes. To achieve superior performance, developers may leverage automatic performance tuning techniques to determine the optimal parameters on the target device. Although existing approaches have shown promising tuning results in their target scenarios, other requirements such as efficiency, portability, and usability should also be considered because of the rapid growth of heterogeneous computing applications and platforms. In this paper, we re-examine the workgroup size tuning problem and propose a novel approach to meet the aforementioned requirements. We abstract the architectural details into a set of hardware parameters so that the proposed approach can be applied without the presence of target devices, which makes it more accessible to developers. The proposed approach is evaluated on 20 OpenCL kernels and six devices, including both CPUs and GPUs. Experimental results demonstrate that, with negligible overhead, our approach filters out 88.6 percent of the possible workgroup sizes on average. Among all the workgroup size candidates, the best- and worst-performing candidates can achieve average performance of 95.5 and 92.1 percent, respectively, compared with the optimal workgroup size.