Deep Neural Networks on Reconfigurable Embedded Systems
A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Big Data and Augmented Intelligence".
Deadline for manuscript submissions: closed (20 June 2022) | Viewed by 11254
Special Issue Editor
Interests: computer architecture; digital systems design; reconfigurable computing; embedded computing; deep learning; deep neural networks
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Smart systems are a topic of intensive research due to its many contributions which improve the quality of human life in healthcare, security and safety, home-living, and many others. Recently, deep neural networks (DNN) have boosted the accuracy and quality of these applications, improving the results of previous machine learning algorithms. DNN are computationally intensive, so cloud computing has been the preferred platform. However, many smart user-centered applications have latency, privacy, and energy constraints not compatible with cloud computing. So, new high-performance embedded systems are needed to run deep neural networks on end devices. These systems must be performance and energy-efficient, and flexible enough to adapt to the fast evolution of DNN. Reconfigurable computing fulfills these requirements by allowing the computing architecture to be tailored for each particular model with high performance and low energy.
This Special Issue aims to collect recent innovations to deploy DNNs on embedded systems with reconfigurable devices. Potential topics include, but are not limited to:
- DNN for embedded systems
- Architectures to run DNN on embedded systems
- FPGA accelerators for DNN on end devices
- Hardware-oriented optimizations of DNNs on FPGA for embedded systems
- Coarse-grained reconfigurable architectures for embedded deep learning
- Design methodologies for DNN on embedded reconfigurable devices
- Design of DNN for IoT devices with reconfigurable computing
- Reconfigurable embedded devices for DNN applied to health, smart-home, etc.
- Embedded DNN for industrial IoT
- DNN for mobile embedded systems and robotics
- Smart wireless sensor networks with DNN
- Reconfigurable embedded systems for smart-city architectures
Dr. Mário Véstias
Guest Editor
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Keywords
- deep learning
- deep neural network
- end devices
- mobile deep learning
- embedded systems
- smart devices
- smart IoT
- reconfigurable computing
- smart wireless systems
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