DC Field | Value | Language |
---|---|---|
dc.contributor.author | Oh, Jin-Wook | ko |
dc.contributor.author | Lee, Seung-Jin | ko |
dc.contributor.author | Yoo, Hoi-Jun | ko |
dc.date.accessioned | 2013-08-08T06:00:31Z | - |
dc.date.available | 2013-08-08T06:00:31Z | - |
dc.date.created | 2013-06-05 | - |
dc.date.created | 2013-06-05 | - |
dc.date.issued | 2013-05 | - |
dc.identifier.citation | IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS, v.21, no.5, pp.921 - 933 | - |
dc.identifier.issn | 1063-8210 | - |
dc.identifier.uri | http://hdl.handle.net/10203/174738 | - |
dc.description.abstract | Object recognition is computationally intensive and it is challenging to meet 30-f/s real-time processing demands under sub-watt low-power constraints of mobile platforms even for heterogeneous many-core architecture. In this paper, an intelligent inference engine (IIE) is proposed as a hardware controller for a many-core processor to satisfy the requirements of low-power real-time object recognition. The IIE exploits learning and inference capabilities of the neurofuzzy system by adopting the versatile adaptive neurofuzzy inference system (VANFIS) with the proposed hardware-oriented learning algorithm. Using the programmable VANFIS, the IIE can configure its hardware topology adaptively for different target classifications. Its architecture contains analog/digital mixed-mode neurofuzzy circuits for updating online parameters to increase attention efficiency of object recognition process. It is implemented in 0.13-mu m CMOS process and achieves 1.2-mW power consumption with 94% average classification accuracy within 1-mu s operation delay. The 0.765-mm(2) IIE achieves 76% attention efficiency and reduces power and processing delay of the 50-mm(2) image processor by up to 37% and 28%, respectively, when 96% recognition accuracy is achieved. | - |
dc.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | NEURAL-NETWORKS | - |
dc.subject | FUZZY | - |
dc.subject | SYSTEM | - |
dc.title | 1.2-mW Online Learning Mixed-Mode Intelligent Inference Engine for Low-Power Real-Time Object Recognition Processor | - |
dc.type | Article | - |
dc.identifier.wosid | 000318172500011 | - |
dc.identifier.scopusid | 2-s2.0-84876806584 | - |
dc.type.rims | ART | - |
dc.citation.volume | 21 | - |
dc.citation.issue | 5 | - |
dc.citation.beginningpage | 921 | - |
dc.citation.endingpage | 933 | - |
dc.citation.publicationname | IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS | - |
dc.identifier.doi | 10.1109/TVLSI.2012.2198249 | - |
dc.embargo.liftdate | 9999-12-31 | - |
dc.embargo.terms | 9999-12-31 | - |
dc.contributor.localauthor | Yoo, Hoi-Jun | - |
dc.contributor.nonIdAuthor | Lee, Seung-Jin | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Mixed-mode processor | - |
dc.subject.keywordAuthor | neurofuzzy | - |
dc.subject.keywordAuthor | object recognition | - |
dc.subject.keywordAuthor | VLSI | - |
dc.subject.keywordPlus | NEURAL-NETWORKS | - |
dc.subject.keywordPlus | FUZZY | - |
dc.subject.keywordPlus | SYSTEM | - |
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