FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2950446178> ?p ?o ?g. }

• W2950446178 abstract "Author(s): Payvand, Melika | Advisor(s): Theogarajan, Luke | Abstract: In the field of neuromorphic VLSI connectivity is a huge bottleneck in implementing brain-inspired circuits due to the large number of synapses needed for performing brain-like functions. (E.g. pattern recognition, classification, etc.). In this thesis I have addressed this problem using a two pronged approach namely spatial and temporal.Spatial: The real-estate occupied by silicon synapses have been an impediment to implementing neuromorphic circuits. In recent years, memristors have emerged as a nano-scale analog synapse. Furthermore, these nano-devices can be integrated on top of CMOS chips enabling the realization of dense neural networks. As a first step in realizing this vision, a programmable CMOS chip enabling direct integration of memristors was realized. In a collaborative MURI project, a CMOS memory platform was designed for the memristive memory array in a hybrid/3D architecture (CMOL architecture) and memristors were successfully integrated on top of it. After demonstrating feasibility of post-CMOS integration of memristors, a second design containing an array of spiking CMOS neurons was designed in a 5mm x 5mm chip in a 180nm CMOS process to explore the role of memristors as synapses in neuromorphic chips.8Temporal: While physical miniaturization by integrating memristors is one facet of realizing area-efficient neural networks, on-chip routing between silicon neurons prevents the complete realization of complex networks containing large number of neurons. A promising solution for the connectivity problem is to employ spatio-temporal coding to encode neuronal information in the time of arrival of the spikes. Temporal codes open up a whole new range of coding schemes which not only are energy efficient (computation with one spike) but also have much larger information capacity than their conventional counterparts. This can result in reducing the number of connections to do similar tasks with traditional rate-based methods.By choosing an efficient temporal coding scheme we developed a system architecture by which pattern classification can be done using a “Winners-share-all” instead of a “Winner-takes-all” mechanism. Winner-takes-all limits the code space to the number of output neurons, meaning n output neurons can only classify n pattern. In winners-share-all we exploit the code space provided by the temporal code by training different combination of k out of n neurons to fire together in response to different patterns. Optimal values of k in order to maximize information capacity using n output neurons were theoretically determined and utilized. An unsupervised network of 3 layers was trained to classify 14 patterns of 15 x 15 pixels while using only 6 output neurons to demonstrate the power of the technique. The reduction in the number of output neurons results in the reduction of number of training parameters and results in lower power, area and memory required for the same functionality." @default.
• W2950446178 created "2019-06-27" @default.
• W2950446178 creator A5025696765 @default.
• W2950446178 date "2016-01-01" @default.
• W2950446178 modified "2023-09-23" @default.
• W2950446178 title "Area-efficient Neuromorphic Silicon Circuits and Architectures using Spatial and Spatio-Temporal Approaches" @default.
• W2950446178 cites W1542981317 @default.
• W2950446178 cites W1594175465 @default.
• W2950446178 cites W1594747477 @default.
• W2950446178 cites W1965970229 @default.
• W2950446178 cites W1990404128 @default.
• W2950446178 cites W1995875735 @default.
• W2950446178 cites W2018774711 @default.
• W2950446178 cites W2023698363 @default.
• W2950446178 cites W2069164217 @default.
• W2950446178 cites W2069233440 @default.
• W2950446178 cites W2103507131 @default.
• W2950446178 cites W2105533736 @default.
• W2950446178 cites W2128924312 @default.
• W2950446178 cites W2132172349 @default.
• W2950446178 cites W2134682148 @default.
• W2950446178 cites W2137249916 @default.
• W2950446178 cites W2145861266 @default.
• W2950446178 cites W2147030841 @default.
• W2950446178 cites W2147101007 @default.
• W2950446178 cites W2152323008 @default.
• W2950446178 cites W2170731400 @default.
• W2950446178 hasPublicationYear "2016" @default.
• W2950446178 type Work @default.
• W2950446178 sameAs 2950446178 @default.
• W2950446178 citedByCount "0" @default.
• W2950446178 crossrefType "journal-article" @default.
• W2950446178 hasAuthorship W2950446178A5025696765 @default.
• W2950446178 hasConcept C118524514 @default.
• W2950446178 hasConcept C127413603 @default.
• W2950446178 hasConcept C149635348 @default.
• W2950446178 hasConcept C150072547 @default.
• W2950446178 hasConcept C151927369 @default.
• W2950446178 hasConcept C154945302 @default.
• W2950446178 hasConcept C24326235 @default.
• W2950446178 hasConcept C2780513914 @default.
• W2950446178 hasConcept C41008148 @default.
• W2950446178 hasConcept C46362747 @default.
• W2950446178 hasConcept C50644808 @default.
• W2950446178 hasConceptScore W2950446178C118524514 @default.
• W2950446178 hasConceptScore W2950446178C127413603 @default.
• W2950446178 hasConceptScore W2950446178C149635348 @default.
• W2950446178 hasConceptScore W2950446178C150072547 @default.
• W2950446178 hasConceptScore W2950446178C151927369 @default.
• W2950446178 hasConceptScore W2950446178C154945302 @default.
• W2950446178 hasConceptScore W2950446178C24326235 @default.
• W2950446178 hasConceptScore W2950446178C2780513914 @default.
• W2950446178 hasConceptScore W2950446178C41008148 @default.
• W2950446178 hasConceptScore W2950446178C46362747 @default.
• W2950446178 hasConceptScore W2950446178C50644808 @default.
• W2950446178 hasLocation W29504461781 @default.
• W2950446178 hasOpenAccess W2950446178 @default.
• W2950446178 hasPrimaryLocation W29504461781 @default.
• W2950446178 hasRelatedWork W1544148687 @default.
• W2950446178 hasRelatedWork W1935332602 @default.
• W2950446178 hasRelatedWork W1950808204 @default.
• W2950446178 hasRelatedWork W2015667993 @default.
• W2950446178 hasRelatedWork W2059054570 @default.
• W2950446178 hasRelatedWork W2062258233 @default.
• W2950446178 hasRelatedWork W2080038686 @default.
• W2950446178 hasRelatedWork W2086432247 @default.
• W2950446178 hasRelatedWork W2131763976 @default.
• W2950446178 hasRelatedWork W2577981465 @default.
• W2950446178 hasRelatedWork W2608368962 @default.
• W2950446178 hasRelatedWork W2750583395 @default.
• W2950446178 hasRelatedWork W2757082811 @default.
• W2950446178 hasRelatedWork W2766107700 @default.
• W2950446178 hasRelatedWork W2963100025 @default.
• W2950446178 hasRelatedWork W2978794388 @default.
• W2950446178 hasRelatedWork W3020591761 @default.
• W2950446178 hasRelatedWork W3023301871 @default.
• W2950446178 hasRelatedWork W3104049105 @default.
• W2950446178 hasRelatedWork W3105841399 @default.
• W2950446178 isParatext "false" @default.
• W2950446178 isRetracted "false" @default.
• W2950446178 magId "2950446178" @default.
• W2950446178 workType "article" @default.