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dc.contributor.editorPortilla, Jorge
dc.contributor.editorOtero, Andres
dc.contributor.editorMujica, Gabriel
dc.date.accessioned2022-06-21T08:39:01Z
dc.date.available2022-06-21T08:39:01Z
dc.date.issued2022
dc.identifierONIX_20220621_9783036542461_82
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/84504
dc.description.abstractThe 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.
dc.languageEnglish
dc.subject.classificationthema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industriesen_US
dc.subject.otherhigh-level synthesis
dc.subject.otherHLS
dc.subject.otherSDSoC
dc.subject.othersupport vector machines
dc.subject.otherSVM
dc.subject.othercode refactoring
dc.subject.otherZynq
dc.subject.otherZedBoard
dc.subject.otherextreme edge
dc.subject.otherembedded edge computing
dc.subject.otherinternet of things deployment
dc.subject.otherhardware design
dc.subject.otherIoT security
dc.subject.otherContiki-NG
dc.subject.othertrustability
dc.subject.otherembedded systems
dc.subject.othercollaborative filtering
dc.subject.otherrecommender systems
dc.subject.otherparallelism
dc.subject.otherreconfigurable hardware
dc.subject.otherneuroevolution
dc.subject.otherblock-based neural network
dc.subject.otherdynamic and partial reconfiguration
dc.subject.otherscalability
dc.subject.otherreinforcement learning
dc.subject.otherembedded system
dc.subject.otherartificial intelligence
dc.subject.otherhardware acceleration
dc.subject.otherneuromorphic processor
dc.subject.otherpower consumption
dc.subject.otherharsh environment
dc.subject.otherfog computing
dc.subject.otheredge computing
dc.subject.othercloud computing
dc.subject.otherIoT gateway
dc.subject.otherLoRa
dc.subject.otherWiFi
dc.subject.otherlow power consumption
dc.subject.otherlow latency
dc.subject.otherflexible
dc.subject.othersmart port
dc.subject.otherquantisation
dc.subject.otherevolutionary algorithm
dc.subject.otherneural network
dc.subject.otherFPGA
dc.subject.otherMovidius VPU
dc.subject.other2D graphics accelerator
dc.subject.otherline-drawing
dc.subject.otherBresenham’s algorithm
dc.subject.otheralpha-blending
dc.subject.otheranti-aliasing
dc.subject.otherfield-programmable gate array
dc.subject.otherdeep learning
dc.subject.otherperformance estimation
dc.subject.otherGaussian process
dc.titleRecent Advances in Embedded Computing, Intelligence and Applications
dc.typebook
oapen.identifier.doi10.3390/books978-3-0365-4245-4
oapen.relation.isPublishedBy46cabcaa-dd94-4bfe-87b4-55023c1b36d0
oapen.relation.isbn9783036542461
oapen.relation.isbn9783036542454
oapen.pages188
oapen.place.publicationBasel


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