LEADER 04544nam 2201045z- 450 001 9910404090703321 005 20210211 010 $a3-03928-577-7 035 $a(CKB)4100000011302231 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/53144 035 $a(oapen)doab53144 035 $a(EXLCZ)994100000011302231 100 $a20202102d2020 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aMemristors for Neuromorphic Circuits and Artificial Intelligence Applications 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2020 215 $a1 online resource (244 p.) 311 08$a3-03928-576-9 330 $aArtificial Intelligence (AI) has found many applications in the past decade due to the ever increasing computing power. Artificial Neural Networks are inspired in the brain structure and consist in the interconnection of artificial neurons through artificial synapses. Training these systems requires huge amounts of data and, after the network is trained, it can recognize unforeseen data and provide useful information. The so-called Spiking Neural Networks behave similarly to how the brain functions and are very energy efficient. Up to this moment, both spiking and conventional neural networks have been implemented in software programs running on conventional computing units. However, this approach requires high computing power, a large physical space and is energy inefficient. Thus, there is an increasing interest in developing AI tools directly implemented in hardware. The first hardware demonstrations have been based on CMOS circuits for neurons and specific communication protocols for synapses. However, to further increase training speed and energy efficiency while decreasing system size, the combination of CMOS neurons with memristor synapses is being explored. The memristor is a resistor with memory which behaves similarly to biological synapses. This book explores the state-of-the-art of neuromorphic circuits implementing neural networks with memristors for AI applications. 606 $aHistory of engineering and technology$2bicssc 610 $aAI 610 $aartificial intelligence 610 $aartificial neural network 610 $aartificial synapse 610 $aautocovariance 610 $aboost-factor adjustment 610 $acharacter recognition 610 $acircuit design 610 $acompact model 610 $acortical neurons 610 $acrossbar array 610 $adeep learning networks 610 $adefect-tolerant spatial pooling 610 $aelectronic synapses 610 $aemulator 610 $aFlash memories 610 $agraphene oxide 610 $ahardware-based deep learning ICs 610 $ahierarchical temporal memory 610 $alaser 610 $amemristive devices 610 $amemristor 610 $amemristor crossbar 610 $amemristor-CMOS hybrid circuit 610 $amemristors 610 $amultiscale modeling 610 $aneocortex 610 $aneural network hardware 610 $aneural networks 610 $aneuromorphic 610 $aneuromorphic computing 610 $aneuromorphic engineering 610 $aneuromorphic hardware 610 $aneuromorphic systems 610 $aneuromorphics 610 $aoptimization 610 $aOxRAM 610 $apattern recognition 610 $apavlov 610 $areinforcement learning 610 $aresistive switching 610 $aRRAM 610 $aself-organization maps 610 $asensory and hippocampal responses 610 $asimulation 610 $aspike-timing-dependent plasticity 610 $aspiking neural network 610 $aspiking neural networks 610 $aSTDP 610 $astrongly correlated oxides 610 $asynapse 610 $asynaptic device 610 $asynaptic plasticity 610 $asynaptic weight 610 $atemporal pooling 610 $atime series modeling 610 $atransistor-like devices 610 $avariability 610 $avertical RRAM 610 $awire resistance 615 7$aHistory of engineering and technology 700 $aSuñé$b Jordi$4auth$01328958 906 $aBOOK 912 $a9910404090703321 996 $aMemristors for Neuromorphic Circuits and Artificial Intelligence Applications$93039226 997 $aUNINA