Live Demonstration: Low-Power Static Neural Network Circuits for Long-Term Change Detection

Low power neural network hardware and its new applications have been explored to exploit its inherent advantage of artificial intelligence in comparison with humans. One such application, long-term change detection, is proposed and presented in this live demonstration. Owing to the low power operati...

Full description

Saved in:
Bibliographic Details
Main Authors: Marukame, Takao, Kitamura, Toshimitsu, Sugino, Junichi, Ishikawa, Kazuo, Takahashi, Koji, Tamura, Yutaka, Nishi, Yoshifumi
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Biological neural networks
Deep learning
Field programmable gate arrays
Monitoring
Neurons
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 1
container_issue
container_start_page 1
container_title
container_volume
creator Marukame, Takao
Kitamura, Toshimitsu
Sugino, Junichi
Ishikawa, Kazuo
Takahashi, Koji
Tamura, Yutaka
Nishi, Yoshifumi
description Low power neural network hardware and its new applications have been explored to exploit its inherent advantage of artificial intelligence in comparison with humans. One such application, long-term change detection, is proposed and presented in this live demonstration. Owing to the low power operation in static analog/digital-mixed neural network circuits, our system using them can detect a change of human-friendly information, e.g., handwritten digits, whereas humans have difficulty noticing a gradual change over the long-term.
doi_str_mv 10.1109/ISCAS.2019.8702246
format conference_proceeding
fullrecord <record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_8702246</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>8702246</ieee_id><sourcerecordid>8702246</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-42338abccd67559506cbb07585bc23807df490cb16d840c3d2a96e55ccc116d43</originalsourceid><addsrcrecordid>eNotkF1LwzAYRgMqOOf-gN7kD7S-SZov70b9GhQVOq-8GGmazujaSppZ_Pd2uKsDh4dz8SB0RSAlBPTNqsyXZUqB6FRJoDQTJ2ihpSKSKgJMS3GKZpRwlRBO-Tm6GIZPAAog6Ay9F_7H4TvX9t0Qg4m-725x0Y_Jaz-6gMs4KYuf3T6Y3YQ49uEL5z7YvY8DbvowjbttsnahxfmH6baHWHT2ELpEZ43ZDW5x5By9Pdyv86ekeHlc5csi8UTymGSUMWUqa2shOdcchK0qkFzxylKmQNZNpsFWRNQqA8tqarRwnFtryeQyNkfX_13vnNt8B9-a8Ls5fsH-AG9lUvk</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Live Demonstration: Low-Power Static Neural Network Circuits for Long-Term Change Detection</title><source>IEEE Xplore All Conference Series</source><creator>Marukame, Takao ; Kitamura, Toshimitsu ; Sugino, Junichi ; Ishikawa, Kazuo ; Takahashi, Koji ; Tamura, Yutaka ; Nishi, Yoshifumi</creator><creatorcontrib>Marukame, Takao ; Kitamura, Toshimitsu ; Sugino, Junichi ; Ishikawa, Kazuo ; Takahashi, Koji ; Tamura, Yutaka ; Nishi, Yoshifumi</creatorcontrib><description>Low power neural network hardware and its new applications have been explored to exploit its inherent advantage of artificial intelligence in comparison with humans. One such application, long-term change detection, is proposed and presented in this live demonstration. Owing to the low power operation in static analog/digital-mixed neural network circuits, our system using them can detect a change of human-friendly information, e.g., handwritten digits, whereas humans have difficulty noticing a gradual change over the long-term.</description><identifier>ISSN: 2158-1525</identifier><identifier>ISBN: 9781728103976</identifier><identifier>ISBN: 1728103975</identifier><identifier>DOI: 10.1109/ISCAS.2019.8702246</identifier><language>eng</language><publisher>IEEE</publisher><subject>Artificial neural networks ; Biological neural networks ; Deep learning ; Field programmable gate arrays ; Monitoring ; Neurons</subject><ispartof>2019 IEEE International Symposium on Circuits and Systems (ISCAS), 2019, p.1-1</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8702246$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,27904,54533,54910</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8702246$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Marukame, Takao</creatorcontrib><creatorcontrib>Kitamura, Toshimitsu</creatorcontrib><creatorcontrib>Sugino, Junichi</creatorcontrib><creatorcontrib>Ishikawa, Kazuo</creatorcontrib><creatorcontrib>Takahashi, Koji</creatorcontrib><creatorcontrib>Tamura, Yutaka</creatorcontrib><creatorcontrib>Nishi, Yoshifumi</creatorcontrib><title>Live Demonstration: Low-Power Static Neural Network Circuits for Long-Term Change Detection</title><title>2019 IEEE International Symposium on Circuits and Systems (ISCAS)</title><addtitle>ISCAS</addtitle><description>Low power neural network hardware and its new applications have been explored to exploit its inherent advantage of artificial intelligence in comparison with humans. One such application, long-term change detection, is proposed and presented in this live demonstration. Owing to the low power operation in static analog/digital-mixed neural network circuits, our system using them can detect a change of human-friendly information, e.g., handwritten digits, whereas humans have difficulty noticing a gradual change over the long-term.</description><subject>Artificial neural networks</subject><subject>Biological neural networks</subject><subject>Deep learning</subject><subject>Field programmable gate arrays</subject><subject>Monitoring</subject><subject>Neurons</subject><issn>2158-1525</issn><isbn>9781728103976</isbn><isbn>1728103975</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2019</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotkF1LwzAYRgMqOOf-gN7kD7S-SZov70b9GhQVOq-8GGmazujaSppZ_Pd2uKsDh4dz8SB0RSAlBPTNqsyXZUqB6FRJoDQTJ2ihpSKSKgJMS3GKZpRwlRBO-Tm6GIZPAAog6Ay9F_7H4TvX9t0Qg4m-725x0Y_Jaz-6gMs4KYuf3T6Y3YQ49uEL5z7YvY8DbvowjbttsnahxfmH6baHWHT2ELpEZ43ZDW5x5By9Pdyv86ekeHlc5csi8UTymGSUMWUqa2shOdcchK0qkFzxylKmQNZNpsFWRNQqA8tqarRwnFtryeQyNkfX_13vnNt8B9-a8Ls5fsH-AG9lUvk</recordid><startdate>201905</startdate><enddate>201905</enddate><creator>Marukame, Takao</creator><creator>Kitamura, Toshimitsu</creator><creator>Sugino, Junichi</creator><creator>Ishikawa, Kazuo</creator><creator>Takahashi, Koji</creator><creator>Tamura, Yutaka</creator><creator>Nishi, Yoshifumi</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>201905</creationdate><title>Live Demonstration: Low-Power Static Neural Network Circuits for Long-Term Change Detection</title><author>Marukame, Takao ; Kitamura, Toshimitsu ; Sugino, Junichi ; Ishikawa, Kazuo ; Takahashi, Koji ; Tamura, Yutaka ; Nishi, Yoshifumi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-42338abccd67559506cbb07585bc23807df490cb16d840c3d2a96e55ccc116d43</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Artificial neural networks</topic><topic>Biological neural networks</topic><topic>Deep learning</topic><topic>Field programmable gate arrays</topic><topic>Monitoring</topic><topic>Neurons</topic><toplevel>online_resources</toplevel><creatorcontrib>Marukame, Takao</creatorcontrib><creatorcontrib>Kitamura, Toshimitsu</creatorcontrib><creatorcontrib>Sugino, Junichi</creatorcontrib><creatorcontrib>Ishikawa, Kazuo</creatorcontrib><creatorcontrib>Takahashi, Koji</creatorcontrib><creatorcontrib>Tamura, Yutaka</creatorcontrib><creatorcontrib>Nishi, Yoshifumi</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Marukame, Takao</au><au>Kitamura, Toshimitsu</au><au>Sugino, Junichi</au><au>Ishikawa, Kazuo</au><au>Takahashi, Koji</au><au>Tamura, Yutaka</au><au>Nishi, Yoshifumi</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Live Demonstration: Low-Power Static Neural Network Circuits for Long-Term Change Detection</atitle><btitle>2019 IEEE International Symposium on Circuits and Systems (ISCAS)</btitle><stitle>ISCAS</stitle><date>2019-05</date><risdate>2019</risdate><spage>1</spage><epage>1</epage><pages>1-1</pages><issn>2158-1525</issn><isbn>9781728103976</isbn><isbn>1728103975</isbn><abstract>Low power neural network hardware and its new applications have been explored to exploit its inherent advantage of artificial intelligence in comparison with humans. One such application, long-term change detection, is proposed and presented in this live demonstration. Owing to the low power operation in static analog/digital-mixed neural network circuits, our system using them can detect a change of human-friendly information, e.g., handwritten digits, whereas humans have difficulty noticing a gradual change over the long-term.</abstract><pub>IEEE</pub><doi>10.1109/ISCAS.2019.8702246</doi><tpages>1</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier ISSN: 2158-1525
ispartof 2019 IEEE International Symposium on Circuits and Systems (ISCAS), 2019, p.1-1
issn 2158-1525
language eng
recordid cdi_ieee_primary_8702246
source IEEE Xplore All Conference Series
subjects Artificial neural networks
Biological neural networks
Deep learning
Field programmable gate arrays
Monitoring
Neurons
title Live Demonstration: Low-Power Static Neural Network Circuits for Long-Term Change Detection
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-27T00%3A06%3A47IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Live%20Demonstration:%20Low-Power%20Static%20Neural%20Network%20Circuits%20for%20Long-Term%20Change%20Detection&rft.btitle=2019%20IEEE%20International%20Symposium%20on%20Circuits%20and%20Systems%20(ISCAS)&rft.au=Marukame,%20Takao&rft.date=2019-05&rft.spage=1&rft.epage=1&rft.pages=1-1&rft.issn=2158-1525&rft.isbn=9781728103976&rft.isbn_list=1728103975&rft_id=info:doi/10.1109/ISCAS.2019.8702246&rft_dat=%3Cieee_CHZPO%3E8702246%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i175t-42338abccd67559506cbb07585bc23807df490cb16d840c3d2a96e55ccc116d43%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=8702246&rfr_iscdi=true