Emerging Computing Paradigms for Efficient Edge AI Acceleration
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Circuit and Signal Processing".
Deadline for manuscript submissions: 15 December 2025 | Viewed by 27
Special Issue Editors
2. Institute of Communication and Computer Systems (ICCS), 15773 Zografou, Greece
Interests: circuits and systems; VLSI design; reconfigurable computing; AI applications; stochastic computing; arithmetic circuits; edge AI
Interests: circuits and systems; emerging technologies; memristors; nonlinear dynamical circuits; stochastic resonance; in-memory computing; unconventional computing; cellular automata; cellular neural networks
Special Issue Information
Dear Colleagues,
The continuous advancements in artificial intelligence (AI) algorithms, particularly deep learning models, have made their integration into modern applications essential. Many of these applications are required to simultaneously operate “at the edge” and process data in real time to avoid latency and bandwidth issues caused by exchanging data with centralized servers. Computing at the edge, however, necessitates hardware-efficient realizations of AI algorithms to accelerate their performance, requiring devices with reduced size and energy consumption, compactness and mass parallelism, all while maintaining optimal computational accuracy. With traditional computing and processing techniques pushing devices to their limits, new and emerging computing paradigms are being explored as solutions to balance the computational accuracy–hardware efficiency trade-off. Driven by the requirements of resource-constrained devices, this Special Issue aims to advance innovative circuits, architectures, systems and signal processing techniques that accelerate AI applications, with emphasis placed on approaches beyond the conventional computing ones.
In this Special Issue, original research articles are welcome. Topics of interest include, but are not limited to, the theory, design, modeling, and application of the following:
- Analog computing;
- Approximate computing;
- Hybrid computing techniques;
- Hyperdimensional computing;
- In-memory and near-memory computing;
- Memristor-based devices and computing;
- Neuromorphic computing;
- Pruning techniques;
- Quantization techniques;
- Reservoir computing;
- Stochastic computing;
- Unconventional computing.
Dr. Nikos Temenos
Dr. Vasileios Ntinas
Guest Editors
Manuscript Submission Information
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Keywords
- hardware efficiency
- in-memory computing
- near-memory computing
- pruning
- quantization
- unconventional computing
- AI accelerators
- circuits and systems
- edge AI
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