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Presentation 2018-04-19 14:55
[Invited Talk] Low power Deep Learning hardware using emerging analog non-volatile memory
Irina Kataeva (DENSO Corp.)
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, Artificial Intelligence, Deep Learning in particular, has become a hot topic of both research and practical applications. However, its success also highlighted its power hungry nature that makes it difficult or even prohibitive to implement for applications where low power real time operation is critical, such as for example self-driving cars. Emerging analog non-volatile memory devices (PCM, ReRAM, FeFET) have been proposed as a promising solution to reduce power consumption by orders of magnitude compared to conventional digital circuits and bring AI applications from cloud to edge. In this talk, we will review recent progress of emerging non-volatile memories for neuro-inspired AI computing and discuss future challenges.
Keyword (in Japanese) (See Japanese page) 
(in English) memristor / Deep Learning / Deep Neural Network / neuromorphic circuit / analog ReRAM / / /  
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Conference Date 2018-04-19 - 2018-04-20 
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Conference Code 2018-04-ICD 
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Title (in English) Low power Deep Learning hardware using emerging analog non-volatile memory 
Sub Title (in English)  
Keyword(1) memristor  
Keyword(2) Deep Learning  
Keyword(3) Deep Neural Network  
Keyword(4) neuromorphic circuit  
Keyword(5) analog ReRAM  
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1st Author's Name Irina Kataeva  
1st Author's Affiliation DENSO CORPORATION (DENSO Corp.)
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Speaker Author-1 
Date Time 2018-04-19 14:55:00 
Presentation Time 50 minutes 
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Volume (vol) vol.118 
Number (no) no.10 
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