A 1.22 TOPS and 1.52 mW/MHz Augmented Reality Multicore Processor With Neural Network NoC for HMD Applications

Cited 21 time in webofscience Cited 22 time in scopus
  • Hit : 365
  • Download : 0
DC FieldValueLanguage
dc.contributor.authorKim, Gyeong-Hoonko
dc.contributor.authorLee, Kyuhoko
dc.contributor.authorKim, Youchangko
dc.contributor.authorPark, Seongwookko
dc.contributor.authorHong, In-Joonko
dc.contributor.authorBong, Kyeongryeolko
dc.contributor.authorYoo, Hoi-Junko
dc.date.accessioned2015-04-07T04:49:19Z-
dc.date.available2015-04-07T04:49:19Z-
dc.date.created2015-02-09-
dc.date.created2015-02-09-
dc.date.created2015-02-09-
dc.date.created2015-02-09-
dc.date.issued2015-01-
dc.identifier.citationIEEE JOURNAL OF SOLID-STATE CIRCUITS, v.50, no.1, pp.113 - 124-
dc.identifier.issn0018-9200-
dc.identifier.urihttp://hdl.handle.net/10203/195212-
dc.description.abstractReal-time augmented reality (AR) is actively studied for the future user interface and experience in high-performance head-mounted display (HMD) systems. The small battery size and limited computing power of the current HMD, however, fail to implement the real-time markerless AR in the HMD. In this paper, we propose a real-time and low-power AR processor for advanced 3D-AR HMD applications. For the high throughput, the processor adopts task-level pipelined SIMD-PE clusters and a congestion-aware network-on-chip (NoC). Both of these two features exploit the high data-level parallelism (DLP) and task-level parallelism (TLP) with the pipelined multicore architecture. For the low power consumption, it employs a vocabulary forest accelerator and a mixed-mode support vector machine (SVM)-based DVFS control to reduce unnecessary external memory accesses and core activation. The proposed 4 mm 8 mm HMD AR processor is fabricated using 65 nm CMOS technology for a battery-powered HMD platform with real-time AR operation. It consumes 381 mW average power and 778 mW peak power at 250 MHz operating frequency and 1.2 V supply voltage. It achieves 1.22 TOPS peak performance and 1.57 TOPS/W energy efficiency, which are, respectively, and higher than the state of the art.-
dc.languageEnglish-
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC-
dc.titleA 1.22 TOPS and 1.52 mW/MHz Augmented Reality Multicore Processor With Neural Network NoC for HMD Applications-
dc.typeArticle-
dc.identifier.wosid000346972800010-
dc.identifier.scopusid2-s2.0-84920144755-
dc.type.rimsART-
dc.citation.volume50-
dc.citation.issue1-
dc.citation.beginningpage113-
dc.citation.endingpage124-
dc.citation.publicationnameIEEE JOURNAL OF SOLID-STATE CIRCUITS-
dc.identifier.doi10.1109/JSSC.2014.2352303-
dc.contributor.localauthorYoo, Hoi-Jun-
dc.contributor.nonIdAuthorBong, Kyeongryeol-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorAugmented reality (AR)-
dc.subject.keywordAuthorAR processor architecture-
dc.subject.keywordAuthorcongestion-aware task assignment-
dc.subject.keywordAuthorheterogeneous SIMD multicore architecture-
dc.subject.keywordAuthor2D-mesh network-on-chip-
dc.subject.keywordPlusOBJECT RECOGNITION-
dc.subject.keywordPlusENGINE-
Appears in Collection
EE-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 21 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0