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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MDPI - Multidisciplinary Digital Publishing Institute
2020
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| LEADER | 04448naaaa2200961uu 4500 | ||
|---|---|---|---|
| 001 | doab_20_500_12854_53144 | ||
| 005 | 20210211 | ||
| 020 | |a books978-3-03928-577-8 | ||
| 020 | |a 9783039285761 | ||
| 020 | |a 9783039285778 | ||
| 024 | 7 | |a 10.3390/books978-3-03928-577-8 |c doi | |
| 041 | 0 | |a English | |
| 042 | |a dc | ||
| 100 | 1 | |a Suñé, Jordi |4 auth | |
| 245 | 1 | 0 | |a Memristors for Neuromorphic Circuits and Artificial Intelligence Applications |
| 260 | |b MDPI - Multidisciplinary Digital Publishing Institute |c 2020 | ||
| 300 | |a 1 electronic resource (244 p.) | ||
| 506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
| 520 | |a 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. | ||
| 540 | |a Creative Commons |f https://creativecommons.org/licenses/by-nc-nd/4.0/ |2 cc |4 https://creativecommons.org/licenses/by-nc-nd/4.0/ | ||
| 546 | |a English | ||
| 653 | |a graphene oxide | ||
| 653 | |a artificial neural network | ||
| 653 | |a simulation | ||
| 653 | |a neural networks | ||
| 653 | |a STDP | ||
| 653 | |a neuromorphics | ||
| 653 | |a spiking neural network | ||
| 653 | |a artificial intelligence | ||
| 653 | |a hierarchical temporal memory | ||
| 653 | |a synaptic weight | ||
| 653 | |a optimization | ||
| 653 | |a transistor-like devices | ||
| 653 | |a multiscale modeling | ||
| 653 | |a memristor crossbar | ||
| 653 | |a spike-timing-dependent plasticity | ||
| 653 | |a memristor-CMOS hybrid circuit | ||
| 653 | |a pavlov | ||
| 653 | |a wire resistance | ||
| 653 | |a AI | ||
| 653 | |a neocortex | ||
| 653 | |a synapse | ||
| 653 | |a character recognition | ||
| 653 | |a resistive switching | ||
| 653 | |a electronic synapses | ||
| 653 | |a defect-tolerant spatial pooling | ||
| 653 | |a emulator | ||
| 653 | |a compact model | ||
| 653 | |a deep learning networks | ||
| 653 | |a artificial synapse | ||
| 653 | |a circuit design | ||
| 653 | |a memristors | ||
| 653 | |a neuromorphic engineering | ||
| 653 | |a memristive devices | ||
| 653 | |a OxRAM | ||
| 653 | |a neural network hardware | ||
| 653 | |a sensory and hippocampal responses | ||
| 653 | |a neuromorphic hardware | ||
| 653 | |a boost-factor adjustment | ||
| 653 | |a RRAM | ||
| 653 | |a variability | ||
| 653 | |a Flash memories | ||
| 653 | |a neuromorphic | ||
| 653 | |a reinforcement learning | ||
| 653 | |a laser | ||
| 653 | |a memristor | ||
| 653 | |a hardware-based deep learning ICs | ||
| 653 | |a temporal pooling | ||
| 653 | |a self-organization maps | ||
| 653 | |a crossbar array | ||
| 653 | |a pattern recognition | ||
| 653 | |a strongly correlated oxides | ||
| 653 | |a vertical RRAM | ||
| 653 | |a autocovariance | ||
| 653 | |a neuromorphic computing | ||
| 653 | |a synaptic device | ||
| 653 | |a cortical neurons | ||
| 653 | |a time series modeling | ||
| 653 | |a spiking neural networks | ||
| 653 | |a neuromorphic systems | ||
| 653 | |a synaptic plasticity | ||
| 856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/2171 |7 0 |z Get Fullteks |
| 856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/53144 |7 0 |z DOAB: description of the publication |