Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
The complete hardware implementation of an optoelectronic neuromorphic computing system is considered as one of the most promising solutions to realize energy-efficient artificial intelligence. Here, a fully light-driven and scalable optoelectronic neuromorphic circuit with metal-chalcogenide/metal-oxide heterostructure phototransistor and photovoltaic divider is proposed. To achieve wavelength-selective neural operation and hardware-based pattern recognition, multispectral light modulated bidirectional synaptic circuits are utilized as an individual pixel for highly accurate and large-area neuromorphic computing system. The wavelength selective control of photo-generated charges at the heterostructure interface enables the bidirectional synaptic modulation behaviors including the excitatory and inhibitory modulations. More importantly, a 7 × 7 neuromorphic pixel circuit array is demonstrated to show the viability of implementing highly accurate hardware-based pattern training. In both the pixel training and pattern recognition simulation, the neuromorphic circuit array with the bidirectional synaptic modulation exhibits lower training errors and higher recognition rates, respectively.
Download full-text PDF |
Source |
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http://dx.doi.org/10.1002/adma.202105017 | DOI Listing |
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