Slow Electronics with Reservoir Computing
This open access book discusses “slow electronics”, the study of devices processing signals with low frequencies. Computers have the remarkable ability to process data at high speeds, but they encounter difficulties when handling signals with low frequencies of less than ~100Hz. They unexpectedly re...
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| Format: | Online |
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| Język: | angielski |
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Springer Nature
2025
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| Hasła przedmiotowe: | |
| Dostęp online: | ONIX_20251128T131701_9789819683833_54 |
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| _version_ | 1869525038288338944 |
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| collection | Directory of Open Access Books |
| description | This open access book discusses “slow electronics”, the study of devices processing signals with low frequencies. Computers have the remarkable ability to process data at high speeds, but they encounter difficulties when handling signals with low frequencies of less than ~100Hz. They unexpectedly require a substantial amount of energy. This poses a challenge for such as biomedical wearables and environmental monitors that need real-time processing of slow signals, especially in energy-limited 'edge’ environments with small batteries. One possible solution to this issue is event-driven processing, which entails the use of non-volatile memory to read/write data and parameters every time a slow (sporadic) signal is detected. However, this approach is highly energy-consuming and unsuitable for the edge environments. To address this challenge, the authors propose “slow electronics” by developing electronic devices and systems that can process low-frequency signals more efficiently. The biological brain is an excellent example of the slow electronics, as it processes low-frequency signals in real time with exceptional energy efficiency. The authors have employed reservoir computing with a spiking neural network (SNN) to simulate the learning and inference of the brain. The integration of slow electronics with SNN reservoir computing allows for real-time data processing in edge environments without an internet connection. This will reveal the determinism or periodicity behind unconscious behaviours and habits that have been difficult to explore due to privacy barriers thus far. Moreover, it may provide a more profound understanding of a craftsman's skills, which they may not even be aware of. This book emphasises the most recent concepts and technological developments in slow electronics. Discussion on the captivating subject of slow electronics are given by delving into the complexities of reservoir calculation, analogue CMOS circuits, artificial neuromorphic devices, and numerical simulation with extended time constants, paving the way for more people-friendly devices in the future. |
| format | Online |
| id | doab-20.500.12854ir-169576 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Springer Nature |
| publisherStr | Springer Nature |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1695762025-11-29T05:24:57Z Slow Electronics with Reservoir Computing Inoue, Isao H. Open Access Neuromorphic Computing Edge Computing Reservoir Computing Slow Electronics Spiking Neural Networks Realtime Learning Low-Frequency Signals thema EDItEUR::U Computing and Information Technology::UK Computer hardware thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics::PBWH Mathematical modelling thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences thema EDItEUR::M Medicine and Nursing::MQ Nursing and ancillary services::MQW Biomedical engineering This open access book discusses “slow electronics”, the study of devices processing signals with low frequencies. Computers have the remarkable ability to process data at high speeds, but they encounter difficulties when handling signals with low frequencies of less than ~100Hz. They unexpectedly require a substantial amount of energy. This poses a challenge for such as biomedical wearables and environmental monitors that need real-time processing of slow signals, especially in energy-limited 'edge’ environments with small batteries. One possible solution to this issue is event-driven processing, which entails the use of non-volatile memory to read/write data and parameters every time a slow (sporadic) signal is detected. However, this approach is highly energy-consuming and unsuitable for the edge environments. To address this challenge, the authors propose “slow electronics” by developing electronic devices and systems that can process low-frequency signals more efficiently. The biological brain is an excellent example of the slow electronics, as it processes low-frequency signals in real time with exceptional energy efficiency. The authors have employed reservoir computing with a spiking neural network (SNN) to simulate the learning and inference of the brain. The integration of slow electronics with SNN reservoir computing allows for real-time data processing in edge environments without an internet connection. This will reveal the determinism or periodicity behind unconscious behaviours and habits that have been difficult to explore due to privacy barriers thus far. Moreover, it may provide a more profound understanding of a craftsman's skills, which they may not even be aware of. This book emphasises the most recent concepts and technological developments in slow electronics. Discussion on the captivating subject of slow electronics are given by delving into the complexities of reservoir calculation, analogue CMOS circuits, artificial neuromorphic devices, and numerical simulation with extended time constants, paving the way for more people-friendly devices in the future. 2025-11-29T05:24:56Z 2025-11-29T05:24:56Z 2025-11-28T12:22:48Z 2026 book ONIX_20251128T131701_9789819683833_54 https://library.oapen.org/handle/20.500.12657/108718 9789819683833 9789819683826 https://directory.doabooks.org/handle/20.500.12854/169576 eng Computer Science; Computer Science (R0) open access image/jpeg n/a https://library.oapen.org/bitstream/20.500.12657/108718/1/9789819683833.pdf Springer Nature Springer 10.1007/978-981-96-8383-3 10.1007/978-981-96-8383-3 9fa3421d-f917-4153-b9ab-fc337c396b5a 86ee1bcf-6367-42c1-80e1-5f7551d3dc2f e8d48ae6-1762-4d18-9739-187a44593243 5059bce2-a6e3-4b46-a84b-903cc413d4a3 1f0de1ea-9a4f-46d5-a9ca-5bbc3290d8fe 9789819683833 9789819683826 Springer 160 Singapore [...] [...] Japan Science and Technology Agency 国立研究開発法人科学技術振興機構 10.13039/501100002241 Japan Society for the Promotion of Science JSPS 10.13039/501100001691 open access |
| spellingShingle | Open Access Neuromorphic Computing Edge Computing Reservoir Computing Slow Electronics Spiking Neural Networks Realtime Learning Low-Frequency Signals thema EDItEUR::U Computing and Information Technology::UK Computer hardware thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics::PBWH Mathematical modelling thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences thema EDItEUR::M Medicine and Nursing::MQ Nursing and ancillary services::MQW Biomedical engineering Slow Electronics with Reservoir Computing |
| title | Slow Electronics with Reservoir Computing |
| title_full | Slow Electronics with Reservoir Computing |
| title_fullStr | Slow Electronics with Reservoir Computing |
| title_full_unstemmed | Slow Electronics with Reservoir Computing |
| title_short | Slow Electronics with Reservoir Computing |
| title_sort | slow electronics with reservoir computing |
| topic | Open Access Neuromorphic Computing Edge Computing Reservoir Computing Slow Electronics Spiking Neural Networks Realtime Learning Low-Frequency Signals thema EDItEUR::U Computing and Information Technology::UK Computer hardware thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics::PBWH Mathematical modelling thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences thema EDItEUR::M Medicine and Nursing::MQ Nursing and ancillary services::MQW Biomedical engineering |
| topic_facet | Open Access Neuromorphic Computing Edge Computing Reservoir Computing Slow Electronics Spiking Neural Networks Realtime Learning Low-Frequency Signals thema EDItEUR::U Computing and Information Technology::UK Computer hardware thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics::PBWH Mathematical modelling thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences thema EDItEUR::M Medicine and Nursing::MQ Nursing and ancillary services::MQW Biomedical engineering |
| url | ONIX_20251128T131701_9789819683833_54 |