SRAM based computing-in-memory for tiny machine learning
This dissertation investigates the potential of Computing-In-Memory (CIM) using Static Random-Access Memory (SRAM) to address the limitations of the Von Neumann architecture and to increase miniaturisation. This research aims to overcome this bottleneck by enabling in-memory computation for tiny mac...
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格式: | Thesis-Master by Coursework |
語言: | English |
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Nanyang Technological University
2024
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在線閱讀: | https://hdl.handle.net/10356/175498 |
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機構: | Nanyang Technological University |
語言: | English |