Slow Electronics with Reservoir Computing
Slow Electronics with Reservoir Computing:
Energy-Efficient Neuromorphic Edge Computing for Low-Frequency Signals
English | 2025 | ISBN: 9819683823 | 164 Pages | PDF EPUB (True) | 44 MB
Energy-Efficient Neuromorphic Edge Computing for Low-Frequency Signals
English | 2025 | ISBN: 9819683823 | 164 Pages | PDF EPUB (True) | 44 MB
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.