| Type of Material: | Thesis |
| Title: | Deep learning model compression and acceleration for resource constrained devices |
| Researcher: | Choudhary, Tejalal |
| Guide: | Mishra, Vipul Kumar | Goswami, Anurag |
| Department: | School of Computer Science Engineering and Technology |
| Publisher: | Bennett University |
| Place: | Greater Noida |
| Year: | 2022 |
| Language: | English |
| Subject: | Computer Science | Computer Science Software Engineering | Engineering and Technology | Computer Science and Information Technology | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; School of Computer Science Engineering and Technology, Bennett University, Greater Noida; 2022 |
| Fulltext: | Shodhganga |
| 003 | IN-AhILN | |
| 005 | 2026-10-14 11:55:33 | |
| 008 | __ | 261014t2022||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_464143 |
| 040 | __ | |dIN-AhILN|aBENN_201310 |
| 041 | __ | |aeng |
| 100 | __ | |eResearcher|aChoudhary, Tejalal |
| 110 | __ | |aSchool of Computer Science Engineering and Technology|0U-0890|dGreater Noida|bBennett University|ein |
| 245 | __ | |aDeep learning model compression and acceleration for resource constrained devices |
| 260 | __ | |aGreater Noida|c2022|bBennett University |
| 502 | __ | |bPhD|d2022|cSchool of Computer Science Engineering and Technology, Bennett University, Greater Noida |
| 518 | __ | |oDate of Award|d2022 |
| 518 | __ | |d2016|oDate of Registration |
| 518 | __ | |d2022|oDate of Completion |
| 520 | __ | |aIn recent years, machine learning and deep learning have shown remarkable improvement in computer vision, natural language processing, stock prediction, weather forecasting, and audio processing, to name a few. The performance of a deep neural network (DNN) is dependent upon a significant number of weight parameters that need to be trained, which is a computational bottleneck. DNNs are also known for their high resource requirements, weight redundancy, and large-scale parameters. Hence, the utilization of DNNs is restricted to devices where adequate resources required to execute them are not available, especially resource-constrained devices such as mobile phones, wearables, and other edge devices. For various practical applications, the trained models should be deployed on resource-constrained devices. Hence, it becomes imperative to compress and accelerate these models before deploying them on resourceconstrained devices while making the least compromise on the model accuracy. To address these challenges, |
| 650 | __ | |aComputer Science and Information Technology|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aComputer Science |
| 653 | __ | |aComputer Science Software Engineering |
| 653 | __ | |aEngineering and Technology |
| 700 | __ | |aMishra, Vipul Kumar|eGuide |
| 700 | __ | |aGoswami, Anurag|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/510513|yShodhganga |
| 905 | __ | |afromsg |
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