Scalable Neuron Circuit Using Conductive-Bridge RAM for Pattern Reconstructions
A novel neuron circuit using a Cu/Ti/Al 2 O 3 -based conductive-bridge random access memory (CBRAM) device for hardware neural networks that utilize nonvolatile memories as synaptic weights is introduced. The neuronal operations are designed and proved using SPICE simulations with a Verilog-A device...
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| Veröffentlicht in: | IEEE transactions on electron devices 2016-06, Vol.63 (6), p.2610-2613 |
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| container_issue | 6 |
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| container_title | IEEE transactions on electron devices |
| container_volume | 63 |
| creator | Jun-Woo Jang Attarimashalkoubeh, Behnoush Prakash, Amit Hyunsang Hwang Yoon-Ha Jeong |
| description | A novel neuron circuit using a Cu/Ti/Al 2 O 3 -based conductive-bridge random access memory (CBRAM) device for hardware neural networks that utilize nonvolatile memories as synaptic weights is introduced. The neuronal operations are designed and proved using SPICE simulations with a Verilog-A device model based on the measured characteristics of the CBRAM device. The applicability of the neuron is demonstrated by constructing a neural network system and applying it to pattern reconstructions that can recall the original patterns from noisy patterns. With these CBRAM-based neurons, a reduction in the area and power of neuromorphic chips is expected in comparison with CMOS-only neuron implementations. |
| doi_str_mv | 10.1109/TED.2016.2549359 |
| format | Article |
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The neuronal operations are designed and proved using SPICE simulations with a Verilog-A device model based on the measured characteristics of the CBRAM device. The applicability of the neuron is demonstrated by constructing a neural network system and applying it to pattern reconstructions that can recall the original patterns from noisy patterns. With these CBRAM-based neurons, a reduction in the area and power of neuromorphic chips is expected in comparison with CMOS-only neuron implementations.</description><subject>Biological neural networks</subject><subject>Circuits</subject><subject>Computer simulation</subject><subject>Conductive-bridge random access memory (CBRAM)</subject><subject>Conductivity</subject><subject>Current measurement</subject><subject>Devices</subject><subject>hardware neural network</subject><subject>integrate-and-fire neuron</subject><subject>Integrated circuit modeling</subject><subject>Neural networks</subject><subject>Neurons</subject><subject>Random access memory</subject><subject>Reconstruction</subject><subject>Resistors</subject><subject>Semiconductor device measurement</subject><issn>0018-9383</issn><issn>1557-9646</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkE1PwzAMhiMEEmNwR-JSiQuXjrhJ0-Q4yviQBkNjO0dZ6k6dunYkLRL_nkybOHCyLT2vZT-EXAMdAVB1v5g8jhIKYpSkXLFUnZABpGkWK8HFKRlQCjJWTLJzcuH9JoyC82RAZp_W1GZVY_SOvWubKK-c7asuWvqqWUd52xS97apvjB9cVawxmo_forJ10YfpOnRNNEfbNr5zeyo0l-SsNLXHq2MdkuXTZJG_xNPZ82s-nsaWKd7FLBMJS1QqjLFmBZlJmEBGOQfJiwQxE9xwqQohirKUUGZScW4YkxJsaUrOhuTusHfn2q8efae3lbdY16bBtvcaJAgaXgQI6O0_dNP2rgnXacgUFVIFOFD0QFnXeu-w1DtXbY370UD13rAOhvXesD4aDpGbQ6RCxD884ymFNGO_UU11iA</recordid><startdate>201606</startdate><enddate>201606</enddate><creator>Jun-Woo Jang</creator><creator>Attarimashalkoubeh, Behnoush</creator><creator>Prakash, Amit</creator><creator>Hyunsang Hwang</creator><creator>Yoon-Ha Jeong</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| ispartof | IEEE transactions on electron devices, 2016-06, Vol.63 (6), p.2610-2613 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Biological neural networks Circuits Computer simulation Conductive-bridge random access memory (CBRAM) Conductivity Current measurement Devices hardware neural network integrate-and-fire neuron Integrated circuit modeling Neural networks Neurons Random access memory Reconstruction Resistors Semiconductor device measurement |
| title | Scalable Neuron Circuit Using Conductive-Bridge RAM for Pattern Reconstructions |
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