Low-Power Hardware Architectures for Machine Learning Applications
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Circuit and Signal Processing".
Deadline for manuscript submissions: 15 February 2025 | Viewed by 120
Special Issue Editors
Interests: analog microelectronic circuits; low-power electronics; biomedical circuits and systems; analog computing, and integrated circuit architectures with applications in artificial intelligence and machine
Interests: design; biomedical instrumentation; electrical impedance tomography; inverse problems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Machine learning (ML) algorithms have become a dominant field in data science, used in a vast variety of applications. Integrating ML on hardware devices significantly reduces the computational costs and offers the capability to process raw data in real time (e.g., from a sensor). Although most hardware applications of ML are presented on digital electronic circuits, the design and implementation of analog circuits for ML is an emerging topic, since ultra-low power consumption combined with extremely fast signal processing can be achieved. Adding analog ML modules to a variety of devices (e.g., biomedical) prevents unnecessary data transferring to clouds or other devices, saving both power and time.
This Special Issue on “Low-Power Hardware Architectures for Machine Learning Applications” focuses on original research papers and comprehensive reviews dealing with the design and implementation of hardware ML modules that effectively post-process signals acquired from wearable or non-wearable devices (e.g., biomedical) targeted to a variety of applications (classification, signal condition, regression, pattern recognition, etc.). Topics of interest for this Special Issue include, but are not limited to, the following:
- Analog, digital, or mixed-signal integrated circuits for low-power prediction systems;
- Analog, digital, or mixed-signal integrated circuits for feature extraction;
- Data pre-processing circuit designs;
- On-memory computing devices;
- Low-power wake-up circuits;
- Neuromorphic circuits and systems for ML applications;
- Biologically inspired sensor systems for ML applications;
- Low-cost device prototypes for pattern recognition.
All these research areas are considered relevant as long as experimentations and/or predictive simulations are the main study drivers.
Dr. Vassilis Alimisis
Dr. Christos Dimas
Guest Editors
Manuscript Submission Information
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Keywords
- machine learning
- hardware design
- pattern recognition
- post-processing
- prediction
- low-power
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