dc.contributor.author | Li, Ling | |
dc.date.accessioned | 2021-07-06T17:28:42Z | |
dc.date.available | 2021-07-06T17:28:42Z | |
dc.date.issued | 2021-07-06T17:28:42Z | |
dc.identifier.uri | http://hdl.handle.net/10222/80583 | |
dc.description.abstract | Approximate multiplier circuit designs have shown substantial advantages in improving many operational features, such as power, area and delay, in many error-resilient applications such as image processing and deep learning applications.
Existing approximate multiplier circuits in this thesis are first reviewed, evaluated, and compared. The comparison results show that the segment-based multiplier has a good trade-off between accuracy and performance by adjusting segment size. A dual segmentation approximate multiplier is then proposed. Compared to the dynamic segment method (DSM)-based approximate multiplier, the proposed design can reduce the energy by 37.90% for 32-bit multipliers, and by 16.68% for 16-bit multipliers. The DSM and proposed multipliers have almost identical accuracy. A merged approximate multiplier with two configurable precisions is proposed for improving the multiplication performance in fixed point convolutional neural networks (CNN) accelerators. Compared with the single-precision approximate multiplier, the merged approximate multiplier achieves significant performance enhancements with minimal accuracy loss. | en_US |
dc.subject | Approximate Computing | en_US |
dc.subject | Approximate Multiplier | en_US |
dc.subject | Accuracy Configurable | en_US |
dc.subject | High Speed | en_US |
dc.subject | Energy Efficient | en_US |
dc.subject | Low Power | en_US |
dc.title | DUAL SEGMENTED AND RECONFIGURABLE APPROXIMATE MULTIPLIERS FOR ERROR-TOLERANT APPLICATIONS | en_US |
dc.date.defence | 2021-06-24 | |
dc.contributor.department | Department of Electrical & Computer Engineering | en_US |
dc.contributor.degree | Master of Applied Science | en_US |
dc.contributor.external-examiner | n/a | en_US |
dc.contributor.graduate-coordinator | Dr. Dmitry Trukhachev | en_US |
dc.contributor.thesis-reader | Dr. William Phillips | en_US |
dc.contributor.thesis-reader | Dr. Jason Gu | en_US |
dc.contributor.thesis-supervisor | Dr. Kamal El-Sankary | en_US |
dc.contributor.ethics-approval | Received | en_US |
dc.contributor.manuscripts | Yes | en_US |
dc.contributor.copyright-release | Yes | en_US |