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dc.contributor.authorGao, Ligangen_US
dc.contributor.authorWang, I-Tingen_US
dc.contributor.authorChen, Pai-Yuen_US
dc.contributor.authorVrudhula, Sarmaen_US
dc.contributor.authorSeo, Jae-Sunen_US
dc.contributor.authorCao, Yuen_US
dc.contributor.authorHou, Tuo-Hungen_US
dc.contributor.authorYu, Shimengen_US
dc.date.accessioned2015-12-02T02:59:34Z-
dc.date.available2015-12-02T02:59:34Z-
dc.date.issued2015-11-13en_US
dc.identifier.issn0957-4484en_US
dc.identifier.urihttp://dx.doi.org/10.1088/0957-4484/26/45/455204en_US
dc.identifier.urihttp://hdl.handle.net/11536/128372-
dc.description.abstractA neuro-inspired computing paradigm beyond the von Neumann architecture is emerging and it generally takes advantage of massive parallelism and is aimed at complex tasks that involve intelligence and learning. The cross-point array architecture with synaptic devices has been proposed for on-chip implementation of the weighted sum and weight update in the learning algorithms. In this work, forming-free, silicon-process-compatible Ta/TaOx/TiO2/Ti synaptic devices are fabricated, in which >200 levels of conductance states could be continuously tuned by identical programming pulses. In order to demonstrate the advantages of parallelism of the cross-point array architecture, a novel fully parallel write scheme is designed and experimentally demonstrated in a small-scale crossbar array to accelerate the weight update in the training process, at a speed that is independent of the array size. Compared to the conventional row-by-row write scheme, it achieves >30x speed-up and >30x improvement in energy efficiency as projected in a large-scale array. If realistic synaptic device characteristics such as device variations are taken into an array-level simulation, the proposed array architecture is able to achieve similar to 95% recognition accuracy of MNIST handwritten digits, which is close to the accuracy achieved by software using the ideal sparse coding algorithm.en_US
dc.language.isoen_USen_US
dc.subjectresistive switchingen_US
dc.subjectneuro-inspired computingen_US
dc.subjectcross-point arrayen_US
dc.subjectsynaptic deviceen_US
dc.subjectonline learningen_US
dc.subjectweight updateen_US
dc.subjectweighted sumen_US
dc.titleFully parallel write/read in resistive synaptic array for accelerating on-chip learningen_US
dc.typeArticleen_US
dc.identifier.doi10.1088/0957-4484/26/45/455204en_US
dc.identifier.journalNANOTECHNOLOGYen_US
dc.citation.volume26en_US
dc.citation.issue45en_US
dc.contributor.department電子工程學系及電子研究所zh_TW
dc.contributor.departmentDepartment of Electronics Engineering and Institute of Electronicsen_US
dc.identifier.wosnumberWOS:000363459900005en_US
dc.citation.woscount0en_US
Appears in Collections:Articles