1 November 1990 Neural network Z-plane implementation with very high interconnection rates
Author Affiliations +
Abstract
Neural networks offer the potential for a quantum leap in the capabilities of imaging sensor systems. The critical neural network implementation factors are: weighted interconnect between all detector outputs; parallel, linear processing of each detector output; fan-out to multiple (thousands) processing nodes per detector output and the ability to independently change interconnect weights and processor node connections within the detector integration times. For a 128 x 128 pixel detector array, the number of desirable interconnects could be as high as iO per second, compared to the approximately iO rates achieved presently with off-focal plane digital processors. Irvine Sensors Corporation (ISC) has conceived a new way of interconnecting 3-D focal plane readout modules and of laying out their component integrated circuits that appears to fulfill the very high interconnect rate requirements. This concept is described and mterconnectivity and other performance attributes are discussed.
© (1990) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
John C. Carson, "Neural network Z-plane implementation with very high interconnection rates", Proc. SPIE 1339, Materials, Devices, Techniques, and Applications for Z-Plane Focal Plane Array Technology II, (1 November 1990); doi: 10.1117/12.23008; https://doi.org/10.1117/12.23008
PROCEEDINGS
4 PAGES


SHARE
KEYWORDS
Sensors

Signal processing

Neural networks

Detector arrays

Artificial neural networks

Information operations

Analog electronics

Back to Top