Article ; Online: Fusion of memristor and digital compute-in-memory processing for energy-efficient edge computing.
2024 Volume 384, Issue 6693, Page(s) 325–332
Abstract: Artificial intelligence (AI) edge devices prefer employing high-capacity nonvolatile compute-in-memory (CIM) to achieve high energy efficiency and rapid wakeup-to-response with sufficient accuracy. Most previous works are based on either memristor-based ... ...
| Abstract | Artificial intelligence (AI) edge devices prefer employing high-capacity nonvolatile compute-in-memory (CIM) to achieve high energy efficiency and rapid wakeup-to-response with sufficient accuracy. Most previous works are based on either memristor-based CIMs, which suffer from accuracy loss and do not support training as a result of limited endurance, or digital static random-access memory (SRAM)-based CIMs, which suffer from large area requirements and volatile storage. We report an AI edge processor that uses a memristor-SRAM CIM-fusion scheme to simultaneously exploit the high accuracy of the digital SRAM CIM and the high energy-efficiency and storage density of the resistive random-access memory memristor CIM. This also enables adaptive local training to accommodate personalized characterization and user environment. The fusion processor achieved high CIM capacity, short wakeup-to-response latency (392 microseconds), high peak energy efficiency (77.64 teraoperations per second per watt), and robust accuracy (<0.5% accuracy loss). This work demonstrates that memristor technology has moved beyond in-lab development stages and now has manufacturability for AI edge processors. |
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| Language | English |
| Publishing date | 2024-04-18 |
| Publishing country | United States |
| Document type | Journal Article |
| ZDB-ID | 128410-1 |
| ISSN | 1095-9203 ; 0036-8075 |
| ISSN (online) | 1095-9203 |
| ISSN | 0036-8075 |
| DOI | 10.1126/science.adf5538 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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