LEADER 04682nam 22006735 450 001 9911046007903321 005 20251101120406.0 010 $a981-9683-83-1 024 7 $a10.1007/978-981-96-8383-3 035 $a(CKB)41997254700041 035 $a(MiAaPQ)EBC32387930 035 $a(Au-PeEL)EBL32387930 035 $a(OCoLC)1549520282 035 $a(DE-He213)978-981-96-8383-3 035 $a(EXLCZ)9941997254700041 100 $a20251101d2026 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSlow Electronics with Reservoir Computing $eEnergy-Efficient Neuromorphic Edge Computing for Low-Frequency Signals /$fedited by Isao H. Inoue 205 $a1st ed. 2026. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2026. 215 $a1 online resource (231 pages) 225 1 $aComputer Science Series 311 08$a981-9683-82-3 327 $aIntroduction: what is ?slow electronics? -- Reservoir Computing Models for Slow Electronics -- Fabricating Elements of Slow Electronics with Functional Materials -- Analog CMOS Implementations of Hardware Neurons for Slow Electronics -- Learning and inference in slow electronics: numerical simulation -- Learning and Inference in slow electronics: FPGA emulation and implementation -- Slow Electronics and Attractor -- Decoding the Unseen, Shaping the Future. . 330 $aThis 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. 410 0$aComputer Science Series 606 $aComputers 606 $aMachine learning 606 $aNeural networks (Computer science) 606 $aNeural circuitry 606 $aBionics 606 $aComputer Hardware 606 $aMachine Learning 606 $aMathematical Models of Cognitive Processes and Neural Networks 606 $aNeural Circuits 606 $aBioinspired Technologies 615 0$aComputers. 615 0$aMachine learning. 615 0$aNeural networks (Computer science) 615 0$aNeural circuitry. 615 0$aBionics. 615 14$aComputer Hardware. 615 24$aMachine Learning. 615 24$aMathematical Models of Cognitive Processes and Neural Networks. 615 24$aNeural Circuits. 615 24$aBioinspired Technologies. 676 $a005.758 700 $aInoue$b Isao H$01863236 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911046007903321 996 $aSlow Electronics with Reservoir Computing$94469765 997 $aUNINA