Analog Integrated Circuits in Edge Computing
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Circuit and Signal Processing".
Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 2841
Special Issue Editors
Interests: CMOS; microelectronics; sensors; FPAA, software engineering; computational neuroscience; spiking neurons; ai
Interests: VLSI; embedded systems; EDA; signal processing; nonlinear circuits and systems
Special Issue Information
Dear Colleagues,
The effective processing of data from sensors is one of the key challenges in the field of modern electronics. Fog computing technologies, which are gaining popularity, require innovative solutions in terms of computing performed in devices (edge computing). Analog CMOS circuits operating close to the data source should play a special role here. Analog accelerators make it possible to implement, among others, vision data compression operations, time waveform filtering, pattern classification, or the conversion of analog signals to digital form.
More and more focus in the field of edge computing is put on reconfigurable digital circuits (FPGAs) and AI implementations with the use of simple microprocessor architectures (TinyML). However, moving calculations closer to the data source requires the implementation of alternative analog solutions: reconfigurable analog circuits (FPAAs), analog IPcores, analog classifiers, preprocessors, and accelerators.
The main goal of this Special Issue is to present effective implementations of analog integrated circuits, hardware-oriented algorithms, design methods, and optimizations of CMOS circuits aimed at processing analog data close to the source. Today, such solutions are particularly desirable in medical applications, implantable chips, low-power electronics, human energy harvesting, autonomous distributed systems, wearable devices, etc.
Dr. Szymon Szczęsny
Dr. Marek Kropidłowski
Dr. Michał Melosik
Guest Editors
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Keywords
- Mixed-signal integrated circuits
- Analog CMOS preprocessors and accelerators
- Field-programmable analog array (FPAAs), analog IPcores
- Edge AI, CMOS implementation of neural networks
- Sensor data analysis, sensor techniques
- Hardware-oriented algorithms for analog signal processing
- Low-power IC, weak-inversion mode, moderate-inversion mode
- Optimization and design automation of analog CMOS circuits
- Analog-to-digital Converters, ∑∆ modulators
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