Recent Advances in Embedded Computing, Intelligence and Applications

• Format: eBook
• Language: English
• Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
• Subjects: Information technology industries; 2D graphics accelerator; alpha-blending; anti-aliasing; artificial intelligence; block-based neural network; Bresenham's algorithm; cloud computing; code refactoring; collaborative filtering; Contiki-NG; deep learning; dynamic and partial reconfiguration; edge computing; embedded edge computing; embedded system; embedded systems; evolutionary algorithm; extreme edge; field-programmable gate array; flexible; fog computing; FPGA; Gaussian process; hardware acceleration; hardware design; harsh environment; high-level synthesis; HLS; internet of things deployment; IoT gateway; IoT security; line-drawing; LoRa; low latency; low power consumption; Movidius VPU; neural network; neuroevolution; neuromorphic processor; parallelism; performance estimation; power consumption; quantisation; recommender systems; reconfigurable hardware; reinforcement learning; scalability; SDSoC; smart port; support vector machines; SVM; trustability; WiFi; ZedBoard; Zynq
• Online Access: Open Access: DOAB: description of the publication; Open Access: DOAB, download the publication
• Physical Description: 1 online resource (188 p.)
• ISBN: 9783036542454; 9783036542461; books978-3-0365-4245-4
• Access: Open Access

The latest proliferation of Internet of Things deployments and edge computing combined with artificial intelligence has led to new exciting application scenarios, where embedded digital devices are essential enablers. Moreover, new powerful and efficient devices are appearing to cope with workloads formerly reserved for the cloud, such as deep learning. These devices allow processing close to where data are generated, avoiding bottlenecks due to communication limitations. The efficient integration of hardware, software and artificial intelligence capabilities deployed in real sensing contexts empowers the edge intelligence paradigm, which will ultimately contribute to the fostering of the offloading processing functionalities to the edge. In this Special Issue, researchers have contributed nine peer-reviewed papers covering a wide range of topics in the area of edge intelligence. Among them are hardware-accelerated implementations of deep neural networks, IoT platforms for extreme edge computing, neuro-evolvable and neuromorphic machine learning, and embedded recommender systems.