Engineering Creativity Award: Optical and Electronic        Implementations of Neural Models
{'Code': '07050000', 'Directorate': {'Abbreviation': 'ENG', 'LongName': 'Directorate For Engineering'}, 'Division': {'Abbreviation': 'EEC', 'LongName': 'Div Of Engineering Education and Centers'}}
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This research project is directed to exploring the possibilities                of implementing large parallel generic neural network                           architectures using both silicon devices and optical technology.                The underlying principle of the new architectures is to take                    advantage of the fact that signal processing in silicon ins an                  advanced and mature technology and to incorporate optics where                  silicon fails, namely, the interconnectivity problem.  These new                architectures make possible the construction of fully integrated,               alterable networks with 1000 neurons using existing technologies.               The next important breakthrough will be to implement a complete                 learning structure with local memory on a single device.  Large,                fully parallel architectures that incorporate a simple learning                 algorithm are delineated and their initial design specified.
{'FirstName': 'Amnon', 'LastName': 'Yariv', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Amnon Yariv', 'EmailAddress': 'ayariv@caltech.edu', 'NSF_ID': '000449599', 'StartDate': '09/01/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'California Institute of Technology', 'CityName': 'PASADENA', 'ZipCode': '911250001', 'PhoneNumber': '6263956219', 'StreetAddress': '1200 E CALIFORNIA BLVD', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'California', 'StateCode': 'CA', 'CONGRESSDISTRICT': '28', 'CONGRESS_DISTRICT_ORG': 'CA28', 'ORG_UEI_NUM': 'U2JMKHNS5TG4', 'ORG_LGL_BUS_NAME': 'CALIFORNIA INSTITUTE OF TECHNOLOGY', 'ORG_PRNT_UEI_NUM': None}
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{'Code': '1360', 'Text': 'EWFD-Eng Workforce Development'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1988&All=true', 'xml': '8811586.xml'}