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Volumn 37, Issue 1, 2017, Pages 15-21

Cognitive computing safety: The new horizon for reliability / the design and evolution of deep learning workloads

Author keywords

cognitive architectures; deep learning; Fathom

Indexed keywords

HARDWARE; MICROWAVE INTEGRATED CIRCUITS;

EID: 85015424903     PISSN: 02721732     EISSN: None     Source Type: Journal    
DOI: 10.1109/MM.2017.2     Document Type: Article
Times cited : (11)

References (18)
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    • Dean, J.1    Barroso, L.A.2
  • 4
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  • 7
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    • B. Reagen et al., "Minerva: Enabling Low-Power, Highly-Accurate Deep Neural Network Accelerators," Proc. 43rd Int'l Symp. Computer Architecture, 2016, pp. 267-278.
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  • 8
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    • S. Han et al., "EIE: Efficient Inference Engine on Compressed Deep Neural Network," Proc. 43rd Int'l Symp. Computer Architecture, 2016, pp. 243-254.
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  • 9
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    • 19 Sept.
    • J. Yoshida, "ARM Raises Bar for Safety, Determinism," EE Times, 19 Sept. 2016; www.eetimes.com /document.asp?doc id/1330483.
    • (2016) EE Times
    • Yoshida, J.1
  • 10
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    • O. Temam, "A Defect-Tolerant Accelerator for Emerging High-Performance Applications," Proc. 39th Int'l Symp. Computer Architecture, 2012, pp. 356-367.
    • (2012) Proc. 39th Int'l Symp. Computer Architecture , pp. 356-367
    • Temam, O.1
  • 11
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    • Understanding fault-tolerant distributed systems
    • F. Cristian, "Understanding Fault-Tolerant Distributed Systems," Comm. ACM, vol. 34, 1993, pp. 56-78.
    • (1993) Comm. ACM , vol.34 , pp. 56-78
    • Cristian, F.1
  • 14
    • 84988317001 scopus 로고    scopus 로고
    • Using multiple input, multiple output formal control to maximize resource efficiency in architectures
    • R.P. Pothukuchi et al., "Using Multiple Input, Multiple Output Formal Control to Maximize Resource Efficiency in Architectures," Proc. ACM/IEEE 43rd Ann. Int'l Symp. Computer Architecture, 2016; doi:10.1109/ISCA.2016.63.
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  • 15
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    • Fathom: Reference workloads for modern deep learning methods
    • R. Adolf et al., "Fathom: Reference Workloads for Modern Deep Learning Methods," Proc. IEEE Int'l Symp. Workload Characterization, 2016; doi:10.1109/IISWC.2016.7581275.
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  • 16
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    • Image net large scale visual recognition challenge
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  • 18
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    • Fathom: Reference workloads for modern deep learning methods
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.