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Patent Searching and Data


Title:
METHOD AND APPARATUS FOR DETERMINING MEMORY REQUIREMENT IN A NETWORK
Document Type and Number:
WIPO Patent Application WO/2019/031858
Kind Code:
A1
Abstract:
The present disclosure disclose method and apparatus for determining memory requirement for processing a DNN model on a device, a method includes receiving a DNN model for an input, wherein the DNN model includes a plurality of processing layers. The method includes generating a network graph of the DNN model. The method includes creating a colored network graph of the DNN model based on the identified execution order of the plurality of processing layers. The colored network graph indicates assignment of at least one memory buffer for storing at least one output of at least one processing layer. The method includes determining at least one buffer reuse overlap possibility across the plurality of processing layers. Based on the determined at least one buffer reuse overlap possibility, the method includes determining and assigning the memory required for processing the DNN model.

Inventors:
MINISKAR NARASINGA RAO (IN)
PASUPULETI SIRISH KUMAR (IN)
GADDE RAJ NARAYANA (IN)
VISHNOI ASHOK (IN)
RAJAGOPAL VASANTHAKUMAR (IN)
RAMASAMY CHANDRA KUMAR (IN)
Application Number:
PCT/KR2018/009055
Publication Date:
February 14, 2019
Filing Date:
August 08, 2018
Export Citation:
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Assignee:
SAMSUNG ELECTRONICS CO LTD (KR)
International Classes:
G06N3/04; G06N3/08
Foreign References:
US20100023708A12010-01-28
US20160379109A12016-12-29
US20160379115A12016-12-29
US5778245A1998-07-07
Other References:
MINSOO RHU ET AL.: "vvDNN: Virtualized Deep Neural Networks for Scalable, Memory-Efficient Neural Network Design", 2016 49TH ANNUAL IEEE/ACM INTERNATIONAL SYMPOSIUM ON MICROARCHITECTURE (MICRO), 15 October 2016 (2016-10-15), XP033022464, Retrieved from the Internet
ERIC HUANG, OPTIMAL RECTANGLE PACKING, 31 December 2010 (2010-12-31)
JORDAN GERGOV, ALGORITHMS FOR COMPILE-TIME MEMORY OPTIMIZATION
See also references of EP 3652681A4
Attorney, Agent or Firm:
LEE, Keon-Joo et al. (KR)
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