Publication: Optimizing stochastic computing based convolutional neural network implementation for image processing applications
| datacite.subject.fos | oecd::Engineering and technology::Electrical engineering, Electronic engineering, Information engineering | |
| dc.contributor.author | Lee, Yang Yang | |
| dc.date.accessioned | 2025-11-27T08:06:21Z | |
| dc.date.available | 2025-11-27T08:06:21Z | |
| dc.date.issued | 2024-02-01 | |
| dc.description.abstract | Stochastic Computing (SC) has a tremendous amount of study on Application Specific Integrated Circuit (ASIC) due to its logic-level design. However, Field- programmable Gate Array (FPGA) could be heavily underutilised with literal ASIC transcoding, making FPGA inefficient in implementing SC. Also, not all convolutional neural network (CNN) algorithms could fit or be incompatible with the SC domain. This research reinvented several FPGA-optimised SC CNN architectures and successfully simulated and implemented them on Xilinx Kintex7 FPGA via High- Level Synthesis (HLS) and with Genetic Algorithm (GA) optimisation. SC multiply- accumulate (MAC) component is 35x to 332x more resource efficient than equivalent fixed-point binary computing MAC, while estimated being 2.967x more resource efficient than the conventional SC method in implementing SC CNN. At least 368x better energy efficiency and 31x higher data throughput than binary computing is achieved while only having 0.14% accuracy loss relative to binary CNN that attained 98.36% accuracy in the MNIST handwriting classification task. The SC CNN early decision termination capability extended the performance envelope exponentially, making SC CNN extremely lucrative for image classification applications on edge devices. However, SC CNN YOLOv3 in license plate (LP) detection is prone to SC noise, degrading the average precision (AP) score to 71.21% compared to binary CNN YOLOv3’s 90.16% AP score, despite having the efficiency and throughput gains of 1054x and 77x, respectively. Thus, SC is ill-suited for CNN regression application. | |
| dc.identifier.uri | https://erepo.usm.my/handle/123456789/23245 | |
| dc.language.iso | en | |
| dc.title | Optimizing stochastic computing based convolutional neural network implementation for image processing applications | |
| dc.type | Resource Types::text::thesis::doctoral thesis | |
| dspace.entity.type | Publication | |
| oairecerif.author.affiliation | Universiti Sains Malaysia |