TY - BOOK ID - 138688494 TI - Recent Advances in Embedded Computing, Intelligence and Applications AU - Portilla, Jorge AU - Otero, Andres AU - Mujica, Gabriel PY - 2022 PB - Basel MDPI - Multidisciplinary Digital Publishing Institute DB - UniCat KW - high-level synthesis KW - HLS KW - SDSoC KW - support vector machines KW - SVM KW - code refactoring KW - Zynq KW - ZedBoard KW - extreme edge KW - embedded edge computing KW - internet of things deployment KW - hardware design KW - IoT security KW - Contiki-NG KW - trustability KW - embedded systems KW - collaborative filtering KW - recommender systems KW - parallelism KW - reconfigurable hardware KW - neuroevolution KW - block-based neural network KW - dynamic and partial reconfiguration KW - scalability KW - reinforcement learning KW - embedded system KW - artificial intelligence KW - hardware acceleration KW - neuromorphic processor KW - power consumption KW - harsh environment KW - fog computing KW - edge computing KW - cloud computing KW - IoT gateway KW - LoRa KW - WiFi KW - low power consumption KW - low latency KW - flexible KW - smart port KW - quantisation KW - evolutionary algorithm KW - neural network KW - FPGA KW - Movidius VPU KW - 2D graphics accelerator KW - line-drawing KW - Bresenham’s algorithm KW - alpha-blending KW - anti-aliasing KW - field-programmable gate array KW - deep learning KW - performance estimation KW - Gaussian process UR - https://www.unicat.be/uniCat?func=search&query=sysid:138688494 AB - 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. ER -