Energy and area-efficient mixed-signal-based SRAM computation-in-memory for AI edge devices
The Ph.D. dissertation focuses on mixed-signal-based SRAM computation-in-memory (CIM) for artificial intelligence (AI) edge devices. CIM is an emerging approach that integrates computation capabilities into memory units, reducing data movement and enhancing performance for memory-intensive tasks lik...
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| Format: | Thesis-Doctor of Philosophy |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/181603 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | The Ph.D. dissertation focuses on mixed-signal-based SRAM computation-in-memory (CIM) for artificial intelligence (AI) edge devices. CIM is an emerging approach that integrates computation capabilities into memory units, reducing data movement and enhancing performance for memory-intensive tasks like deep neural networks (DNNs). CIM mainly deals with multiply-and-accumulate (MAC) operations which consist most of the computing burden in DNN. Additionally, mixed-signal-based CIM has many advantages over digital-based CIM in terms of area, throughput, and power in low-precision DNN (1-4b) as able to accumulate multiplication results at once with analog domain (voltage, current and charge, etc.) Therefore, mixed-signal-based CIM is an attractive option as low-precision DNNs have achieved competitive precision so far. |
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