Analog and Digital Circuit Design Techniques and Systems for Machine Learning
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: closed (31 January 2020) | Viewed by 7870
Special Issue Editor
Special Issue Information
Dear Colleagues,
In recent years, machine learning has emerged as a ubiquitous tool for analysis of data and providing actionable insights across a wide range of applications from healthcare, automotive, and environmental monitoring to agriculture. While traditionally, machine learning algorithms are designed to run on computers or server nodes in data centers, there is lot of research interest in incorporating machine learning algorithms in embedded systems as well as accelerating machine learning algorithms through better hardware and hardware–software co-design. Applications of embedded systems with machine learning are in edge/sensor nodes, where incorporation of artificial intelligence can reduce transmission bandwidth and improve security while providing inference capability at the sensor node with reduced latency. At the data center end, better hardware designs can improve throughput and reduce energy consumption through novel circuits/architecture/devices beyond conventional computing.
The aim of this Special Issue is to seek high-quality contributions that highlight circuit and system level techniques to improve energy, throughput, and security of machine learning systems for emerging applications. The topics of interest include but are not limited to:
- Analog signal processing circuits and algorithms for machine learning applications;
- Machine learning circuits for wearable health monitors;
- Machine learning architectures and circuits using emerging devices and circuits, e.g., non-volatile memory devices, compute-in-memory, etc.;
- Neuromorphic computing, e.g., spiking neural networks;
- Advances in system design and machine learning to improve performance and security;
- Circuit design for low-cost recurrent neural networks, including echo states.
Dr. Arindam Sanyal
Guest Editor
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Keywords
- circuit design
- machine learning
- wearable health monitor
- neuromorphic computing
- in-memory computing
- echo state
- spiking neural network
- artificial neural network
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