A Kernel Unfolding Approach over NVM Crossbar Accelerators for Convolutional Neural Networks
| Autor: | Yueh-Han Wu, 巫岳翰 |
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| Rok vydání: | 2019 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 107 A number of recent woks aimed to design accelerators for Convolutional Neural Networks (CNNs) to improve the performance of current Von-Neumann architecture. As computation power keeps growing and memory speed grows relatively slow, data movement between computing units and memory devices has become bottleneck that limit the performance of these accelerators. To eliminate the data movement, Processing-In-Memory (PIM) architecture is widely advocated. However, PIM can only decrease the data movement from off-chip. To further decrease the on-chip data movement, we propose a kernel-unfolding approach to trade off computation power for lower input data movement amount by fully utilize the input feature map data without overlapped data be input. The proposed approach yields improvements of 16.2×, 1.62× and 19.2× in cycle used, execution time, input data amount, respectively. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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