Computing Systems for Embedded Deep Learning
A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Smart System Infrastructure and Applications".
Deadline for manuscript submissions: closed (20 March 2024) | Viewed by 4357
Special Issue Editors
Interests: computer architecture; digital system design; reconfigurable computing; embedded computing; machine learning; deep learning; deep neural networks
Special Issues, Collections and Topics in MDPI journals
Interests: computer architecture; computer arithmetic; residue number systems
Special Issue Information
Dear Colleagues,
Smart embedded systems have recently been the focus of intensive research and development due to their significant potential to improve the quality of human life in areas such as healthcare, security and safety, home living, city living and many others. Machine learning algorithms can be used to design intelligent embedded devices. Recently, deep neural networks (DNN), such as CNN, RNN, LSTM, GAN and SNN, have achieved superior performances in accuracy when compared to other machine learning algorithms, but they require more computing and memory resources, as well as energy. However, computing platforms of embedded systems have limited computing power, memory and energy. So, the implementation of DNNs in embedded systems is currently a major topic in the scientific community and requires further innovation in its development and application. This Special Issue aims to collect recent research with a focus on deploying DNNs in embedded computing systems. Potential topics include, but are not limited to:
- DNN models for embedded systems;
- Optimization of DNN models for embedded computing;
- Quantization and sparsification of DNN models;
- Implementation of DNN in embedded GPUs;
- Implementation of DNN in low-cost computing platforms;
- Reconfigurable architectures for DNN in embedded systems;
- Very low-power embedded platforms for DNNs;
- Coarse-grained reconfigurable architectures for embedded deep learning;
- Design methodologies for DNN on embedded systems;
- Design of DNN for IoT devices;
- Software tools to help design smart embedded systems;
- Applications of DNN on health, smart homes, smart cities, security, surveillance, etc.;
- Smart embedded systems for industrial IoT;
- Designing DNN for robotics.
Dr. Mário Véstias
Dr. Pedro Miguel Florindo Miguens Matutino
Guest Editors
Manuscript Submission Information
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Keywords
- deep learning
- deep neural network
- end devices
- mobile deep learning
- smart embedded systems
- smart devices
- smart IoT
- smart wireless systems
