Memristors for Neuromorphic Circuits and Artificial Intelligence Applications
Artificial 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...
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| מחבר ראשי: | |
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| פורמט: | Online |
| שפה: | אנגלית |
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MDPI - Multidisciplinary Digital Publishing Institute
2021
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| נושאים: | |
| גישה מקוונת: | 45996 |
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| _version_ | 1869521025669005312 |
|---|---|
| author | Suñé, Jordi |
| author_browse | Suñé, Jordi |
| author_facet | Suñé, Jordi |
| author_sort | Suñé, Jordi |
| collection | Directory of Open Access Books |
| description | Artificial 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. |
| format | Online |
| id | doab-20.500.12854ir-53144 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-531442024-04-11T15:10:15Z Memristors for Neuromorphic Circuits and Artificial Intelligence Applications Suñé, Jordi TA1-2040 T1-995 graphene oxide artificial neural network simulation neural networks STDP neuromorphics spiking neural network artificial intelligence hierarchical temporal memory synaptic weight optimization transistor-like devices multiscale modeling memristor crossbar spike-timing-dependent plasticity memristor-CMOS hybrid circuit pavlov wire resistance AI neocortex synapse character recognition resistive switching electronic synapses defect-tolerant spatial pooling emulator compact model deep learning networks artificial synapse circuit design memristors neuromorphic engineering memristive devices OxRAM neural network hardware sensory and hippocampal responses neuromorphic hardware boost-factor adjustment RRAM variability Flash memories neuromorphic reinforcement learning laser memristor hardware-based deep learning ICs temporal pooling self-organization maps crossbar array pattern recognition strongly correlated oxides vertical RRAM autocovariance neuromorphic computing synaptic device cortical neurons time series modeling spiking neural networks neuromorphic systems synaptic plasticity thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology Artificial 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. 2021-02-11T19:15:13Z 2021-02-11T19:15:13Z 2020-06-09 16:38:57 2020 book 45996 9783039285761 9783039285778 https://directory.doabooks.org/handle/20.500.12854/53144 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/2171 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-03928-577-8 10.3390/books978-3-03928-577-8 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783039285761 9783039285778 244 open access |
| spellingShingle | TA1-2040 T1-995 graphene oxide artificial neural network simulation neural networks STDP neuromorphics spiking neural network artificial intelligence hierarchical temporal memory synaptic weight optimization transistor-like devices multiscale modeling memristor crossbar spike-timing-dependent plasticity memristor-CMOS hybrid circuit pavlov wire resistance AI neocortex synapse character recognition resistive switching electronic synapses defect-tolerant spatial pooling emulator compact model deep learning networks artificial synapse circuit design memristors neuromorphic engineering memristive devices OxRAM neural network hardware sensory and hippocampal responses neuromorphic hardware boost-factor adjustment RRAM variability Flash memories neuromorphic reinforcement learning laser memristor hardware-based deep learning ICs temporal pooling self-organization maps crossbar array pattern recognition strongly correlated oxides vertical RRAM autocovariance neuromorphic computing synaptic device cortical neurons time series modeling spiking neural networks neuromorphic systems synaptic plasticity thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology Suñé, Jordi Memristors for Neuromorphic Circuits and Artificial Intelligence Applications |
| title | Memristors for Neuromorphic Circuits and Artificial Intelligence Applications |
| title_full | Memristors for Neuromorphic Circuits and Artificial Intelligence Applications |
| title_fullStr | Memristors for Neuromorphic Circuits and Artificial Intelligence Applications |
| title_full_unstemmed | Memristors for Neuromorphic Circuits and Artificial Intelligence Applications |
| title_short | Memristors for Neuromorphic Circuits and Artificial Intelligence Applications |
| title_sort | memristors for neuromorphic circuits and artificial intelligence applications |
| topic | TA1-2040 T1-995 graphene oxide artificial neural network simulation neural networks STDP neuromorphics spiking neural network artificial intelligence hierarchical temporal memory synaptic weight optimization transistor-like devices multiscale modeling memristor crossbar spike-timing-dependent plasticity memristor-CMOS hybrid circuit pavlov wire resistance AI neocortex synapse character recognition resistive switching electronic synapses defect-tolerant spatial pooling emulator compact model deep learning networks artificial synapse circuit design memristors neuromorphic engineering memristive devices OxRAM neural network hardware sensory and hippocampal responses neuromorphic hardware boost-factor adjustment RRAM variability Flash memories neuromorphic reinforcement learning laser memristor hardware-based deep learning ICs temporal pooling self-organization maps crossbar array pattern recognition strongly correlated oxides vertical RRAM autocovariance neuromorphic computing synaptic device cortical neurons time series modeling spiking neural networks neuromorphic systems synaptic plasticity thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| topic_facet | TA1-2040 T1-995 graphene oxide artificial neural network simulation neural networks STDP neuromorphics spiking neural network artificial intelligence hierarchical temporal memory synaptic weight optimization transistor-like devices multiscale modeling memristor crossbar spike-timing-dependent plasticity memristor-CMOS hybrid circuit pavlov wire resistance AI neocortex synapse character recognition resistive switching electronic synapses defect-tolerant spatial pooling emulator compact model deep learning networks artificial synapse circuit design memristors neuromorphic engineering memristive devices OxRAM neural network hardware sensory and hippocampal responses neuromorphic hardware boost-factor adjustment RRAM variability Flash memories neuromorphic reinforcement learning laser memristor hardware-based deep learning ICs temporal pooling self-organization maps crossbar array pattern recognition strongly correlated oxides vertical RRAM autocovariance neuromorphic computing synaptic device cortical neurons time series modeling spiking neural networks neuromorphic systems synaptic plasticity thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| url | 45996 |
| work_keys_str_mv | AT sunejordi memristorsforneuromorphiccircuitsandartificialintelligenceapplications |