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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.
Sommario
Introduction: 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.
Info autore
Isao H. Inoue received his degrees in physics from the University of Tokyo (BSc 1990, MSc 1992) and began his research career in 1992 at the Electrotechnical Laboratory, which later became part of AIST. He focused on strongly correlated electron systems, particularly Mott transitions, and was among the first to report superconductivity in La-doped SrTiO₃. He received his PhD in 1999 and subsequently spent two years at the University of Cambridge. Upon returning to AIST, he expanded his work from quantum materials to oxide electronics and neuromorphic devices. He developed oxide-based artificial neurons and introduced the concept of “Slow Electronics,” which leverages slow ionic processes for ultra-low-power computing. He also serves as a Professor at the University of Tsukuba and a Visiting Professor at Tokyo University of Science, continuing to lead interdisciplinary research at the intersection of physics and brain-inspired electronics.
Riassunto
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.
