Buchumschlag

Recent Advances in Embedded Computing, Intelligence and Applications

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...

Ausführliche Beschreibung

Gespeichert in:
Format: Online
Sprache:Englisch
Veröffentlicht: MDPI - Multidisciplinary Digital Publishing Institute 2022
Schlagworte:
high-level synthesis
HLS
SDSoC
support vector machines
SVM
code refactoring
Zynq
ZedBoard
extreme edge
embedded edge computing
internet of things deployment
hardware design
IoT security
Contiki-NG
trustability
embedded systems
collaborative filtering
recommender systems
parallelism
reconfigurable hardware
neuroevolution
block-based neural network
dynamic and partial reconfiguration
scalability
reinforcement learning
embedded system
artificial intelligence
hardware acceleration
neuromorphic processor
power consumption
harsh environment
fog computing
edge computing
cloud computing
IoT gateway
LoRa
WiFi
low power consumption
low latency
flexible
smart port
quantisation
evolutionary algorithm
neural network
FPGA
Movidius VPU
2D graphics accelerator
line-drawing
Bresenham’s algorithm
alpha-blending
anti-aliasing
field-programmable gate array
deep learning
performance estimation
Gaussian process
thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries
Online-Zugang:ONIX_20220621_9783036542461_82
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Zusammenfassung: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.

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