000			02133nam a22002537a 4500
005			20260807152528.0
008			260807s2024 |||||||| |||| 00| 0 eng d
020			_a9788770041027
040			_cPK-LaUMT
082			_a006.31 _bADV-
245	0	0	_aAdvancing edge artificial intelligence : _bsystem contexts / _cedited by Ovidiu Vermesan and Dave Marples
260			_aLondon : _bRiver Publishers, _c2024
300			_axxv, 232 p.
490			_aRiver Publishers series in communications and networking
500			_aIncludes bibliographical references and index.
520			_aThe intersection of AI, the Internet of Things (IoT) and edge computing has kindled the edge AI revolution that promises to redefine how we perceive and interact with the physical world through intelligent devices. Edge AI moves intelligence from the network centre to the devices at its edge, entrusting these endpoints to analyse data locally, make decisions, and provide real-time responses. Recent advances in power-efficient high-performance embedded silicon make edge AI a viable proposition, albeit one requiring new distributed architectures and novel design concepts. Moving decision-making closer to the edge makes responses faster and systems more reliable, while the constant pressure to reduce network bandwidth demand and the need to contain spiralling data storage and operations costs help justify the engineering investment necessary to embrace this new paradigm. Moving to decentralised operation opens the door to a multitude of novel applications, covering immersive technologies and autonomous systems across fields as diverse as healthcare and industrial automation, personal assistance and prognostics, surgery, and process control. In the best tradition of systems engineering, the first stage of this transition process is understanding the application domain for edge AI deployment, the ""system context"".
546			_aEng
650			_aArtificial Intelligence
650			_aEdge computing _914151
650			_aMachine learning
700	1		_aVermesan, Ovidiu _914916
700	1		_aMarples, Dave _914917
942			_cBK
999			_c141986 _d141986