Neuromorphic system using capacitor synapses
Abstract Artificial intelligences are indispensable social infrastructures, neural networks are embodiment methodologies, and neuromorphic systems are promising solutions for compact size and low energy. Memristors were first prepared for the synapse devices but incur energy consumption, and memcapa...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-87924-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832571648301072384 |
|---|---|
| author | Reon Oshio Takumi Kuwahara Takeru Aoki Mutsumi Kimura Yasuhiko Nakashima |
| author_facet | Reon Oshio Takumi Kuwahara Takeru Aoki Mutsumi Kimura Yasuhiko Nakashima |
| author_sort | Reon Oshio |
| collection | DOAJ |
| description | Abstract Artificial intelligences are indispensable social infrastructures, neural networks are embodiment methodologies, and neuromorphic systems are promising solutions for compact size and low energy. Memristors were first prepared for the synapse devices but incur energy consumption, and memcapacitors were next prepared but have small dynamic ranges of capacitance. In this research, we have developed a neuromorphic system using capacitor synapses. Here, multiple capacitors have binary-weighted capacitances and are controlled to be connected to intermediate signals. They are discharged through transistors, and when they fall below the threshold voltage, the output signals are inverted. After all, electric charges in the multiple capacitances are summed and measured by the inverting intervals, which is the same as multiply–accumulate operation. A large-scale integration chip is actually fabricated. The working is confirmed by MNIST, and the circuit-aware rounding improves the accuracy to 96%, indicating a sufficient possibility for practical applications, and the energy efficiency is 163 GOPS/W even by the 180 nm technology, indicating a great potential for low energy consumption. |
| format | Article |
| id | doaj-art-4395fe0af41941cfa92cf668f70a51e5 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-4395fe0af41941cfa92cf668f70a51e52025-02-02T12:24:38ZengNature PortfolioScientific Reports2045-23222025-01-011511910.1038/s41598-025-87924-6Neuromorphic system using capacitor synapsesReon Oshio0Takumi Kuwahara1Takeru Aoki2Mutsumi Kimura3Yasuhiko Nakashima4Nara Institute of Science and Technology (NAIST)Nara Institute of Science and Technology (NAIST)Ryukoku UniversityNara Institute of Science and Technology (NAIST)Nara Institute of Science and Technology (NAIST)Abstract Artificial intelligences are indispensable social infrastructures, neural networks are embodiment methodologies, and neuromorphic systems are promising solutions for compact size and low energy. Memristors were first prepared for the synapse devices but incur energy consumption, and memcapacitors were next prepared but have small dynamic ranges of capacitance. In this research, we have developed a neuromorphic system using capacitor synapses. Here, multiple capacitors have binary-weighted capacitances and are controlled to be connected to intermediate signals. They are discharged through transistors, and when they fall below the threshold voltage, the output signals are inverted. After all, electric charges in the multiple capacitances are summed and measured by the inverting intervals, which is the same as multiply–accumulate operation. A large-scale integration chip is actually fabricated. The working is confirmed by MNIST, and the circuit-aware rounding improves the accuracy to 96%, indicating a sufficient possibility for practical applications, and the energy efficiency is 163 GOPS/W even by the 180 nm technology, indicating a great potential for low energy consumption.https://doi.org/10.1038/s41598-025-87924-6 |
| spellingShingle | Reon Oshio Takumi Kuwahara Takeru Aoki Mutsumi Kimura Yasuhiko Nakashima Neuromorphic system using capacitor synapses Scientific Reports |
| title | Neuromorphic system using capacitor synapses |
| title_full | Neuromorphic system using capacitor synapses |
| title_fullStr | Neuromorphic system using capacitor synapses |
| title_full_unstemmed | Neuromorphic system using capacitor synapses |
| title_short | Neuromorphic system using capacitor synapses |
| title_sort | neuromorphic system using capacitor synapses |
| url | https://doi.org/10.1038/s41598-025-87924-6 |
| work_keys_str_mv | AT reonoshio neuromorphicsystemusingcapacitorsynapses AT takumikuwahara neuromorphicsystemusingcapacitorsynapses AT takeruaoki neuromorphicsystemusingcapacitorsynapses AT mutsumikimura neuromorphicsystemusingcapacitorsynapses AT yasuhikonakashima neuromorphicsystemusingcapacitorsynapses |