A near-threshold memristive computing-in-memory engine for edge intelligence
Abstract Memristive computing-in-memory and near-threshold computing are two unconventional computing paradigms that can potentially enhance the energy efficiency and real-time performance of edge devices. However, their scalability faces challenges, primarily due to process variation. Here, we repo...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-61025-4 |
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| author | Linfang Wang Weizeng Li Zhidao Zhou Junjie An Wang Ye Zhi Li Hanghang Gao Hongyang Hu Jing Liu Xiaoming Chen Ling Li Qi Liu Mingoo Seok Chunmeng Dou Ming Liu |
| author_facet | Linfang Wang Weizeng Li Zhidao Zhou Junjie An Wang Ye Zhi Li Hanghang Gao Hongyang Hu Jing Liu Xiaoming Chen Ling Li Qi Liu Mingoo Seok Chunmeng Dou Ming Liu |
| author_sort | Linfang Wang |
| collection | DOAJ |
| description | Abstract Memristive computing-in-memory and near-threshold computing are two unconventional computing paradigms that can potentially enhance the energy efficiency and real-time performance of edge devices. However, their scalability faces challenges, primarily due to process variation. Here, we report a 1-Mb, 16-macro near-threshold memristive computing-in-memory engine. The two-transistor-one-resistor cells provide strong cell current modulation capability with more than 120-times amplified resistance ratio. To mitigate variation issues, we compensate for transistor mismatches by leveraging the intrinsic variations in memristors. Additionally, we propose a charge stacking technique between multiple analog-to-digital converters to perform analog weight-and-combine operations with small energy and area overhead. Moreover, we introduce an inter-macro hybrid control scheme to reduce the task-level inference power. The fabricated chip can perform highly parallel analog computing over 256 input channels with a 2.4% relative standard deviation. It achieves a throughput up to 10.49 tera-operations per second and an energy efficiency up to 88.51 tera-operations per second per watt. |
| format | Article |
| id | doaj-art-0aeb71f5872e462b8814648b36e6cfb3 |
| institution | DOAJ |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-0aeb71f5872e462b8814648b36e6cfb32025-08-20T03:03:28ZengNature PortfolioNature Communications2041-17232025-07-0116111010.1038/s41467-025-61025-4A near-threshold memristive computing-in-memory engine for edge intelligenceLinfang Wang0Weizeng Li1Zhidao Zhou2Junjie An3Wang Ye4Zhi Li5Hanghang Gao6Hongyang Hu7Jing Liu8Xiaoming Chen9Ling Li10Qi Liu11Mingoo Seok12Chunmeng Dou13Ming Liu14State Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics of the Chinese Academy of SciencesState Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics of the Chinese Academy of SciencesState Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics of the Chinese Academy of SciencesState Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics of the Chinese Academy of SciencesState Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics of the Chinese Academy of SciencesState Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics of the Chinese Academy of SciencesState Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics of the Chinese Academy of SciencesState Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics of the Chinese Academy of SciencesState Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics of the Chinese Academy of SciencesInstitute of Computing Technology of the Chinese Academy of SciencesInstitute of Software of the Chinese Academy of SciencesState Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics of the Chinese Academy of SciencesDepartment of Electrical Engineering, Columbia UniversityState Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics of the Chinese Academy of SciencesState Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics of the Chinese Academy of SciencesAbstract Memristive computing-in-memory and near-threshold computing are two unconventional computing paradigms that can potentially enhance the energy efficiency and real-time performance of edge devices. However, their scalability faces challenges, primarily due to process variation. Here, we report a 1-Mb, 16-macro near-threshold memristive computing-in-memory engine. The two-transistor-one-resistor cells provide strong cell current modulation capability with more than 120-times amplified resistance ratio. To mitigate variation issues, we compensate for transistor mismatches by leveraging the intrinsic variations in memristors. Additionally, we propose a charge stacking technique between multiple analog-to-digital converters to perform analog weight-and-combine operations with small energy and area overhead. Moreover, we introduce an inter-macro hybrid control scheme to reduce the task-level inference power. The fabricated chip can perform highly parallel analog computing over 256 input channels with a 2.4% relative standard deviation. It achieves a throughput up to 10.49 tera-operations per second and an energy efficiency up to 88.51 tera-operations per second per watt.https://doi.org/10.1038/s41467-025-61025-4 |
| spellingShingle | Linfang Wang Weizeng Li Zhidao Zhou Junjie An Wang Ye Zhi Li Hanghang Gao Hongyang Hu Jing Liu Xiaoming Chen Ling Li Qi Liu Mingoo Seok Chunmeng Dou Ming Liu A near-threshold memristive computing-in-memory engine for edge intelligence Nature Communications |
| title | A near-threshold memristive computing-in-memory engine for edge intelligence |
| title_full | A near-threshold memristive computing-in-memory engine for edge intelligence |
| title_fullStr | A near-threshold memristive computing-in-memory engine for edge intelligence |
| title_full_unstemmed | A near-threshold memristive computing-in-memory engine for edge intelligence |
| title_short | A near-threshold memristive computing-in-memory engine for edge intelligence |
| title_sort | near threshold memristive computing in memory engine for edge intelligence |
| url | https://doi.org/10.1038/s41467-025-61025-4 |
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