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Title: Neural Inspired Computation Remote Sensing Platform.

Abstract

Remote sensing (RS) data collection capabilities are rapidly evolving hyper-spectrally (sensing more spectral bands), hyper-temporally (faster sampling rates) and hyper-spatially (increasing number of smaller pixels). Accordingly, sensor technologies have outpaced transmission capa- bilities introducing a need to process more data at the sensor. While many sophisticated data processing capabilities are emerging, power and other hardware requirements for these approaches on conventional electronic systems place them out of context for resource constrained operational environments. To address these limitations, in this research effort we have investigated and char- acterized neural-inspired architectures to determine suitability for implementing RS algorithms In doing so, we have been able to highlight a 100x performance per watt improvement using neu- romorphic computing as well as developed an algorithmic architecture co-design and exploration capability.

Authors:
; ; ; ; ;
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1569155
Report Number(s):
SAND2019-11291
679694
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English

Citation Formats

Vineyard, Craig Michael, Severa, William Mark, Green, Sam, Dellana, Ryan, Plagge, Mark, and Hill, Aaron Jamison. Neural Inspired Computation Remote Sensing Platform.. United States: N. p., 2019. Web. doi:10.2172/1569155.
Vineyard, Craig Michael, Severa, William Mark, Green, Sam, Dellana, Ryan, Plagge, Mark, & Hill, Aaron Jamison. Neural Inspired Computation Remote Sensing Platform.. United States. doi:10.2172/1569155.
Vineyard, Craig Michael, Severa, William Mark, Green, Sam, Dellana, Ryan, Plagge, Mark, and Hill, Aaron Jamison. Sun . "Neural Inspired Computation Remote Sensing Platform.". United States. doi:10.2172/1569155. https://www.osti.gov/servlets/purl/1569155.
@article{osti_1569155,
title = {Neural Inspired Computation Remote Sensing Platform.},
author = {Vineyard, Craig Michael and Severa, William Mark and Green, Sam and Dellana, Ryan and Plagge, Mark and Hill, Aaron Jamison},
abstractNote = {Remote sensing (RS) data collection capabilities are rapidly evolving hyper-spectrally (sensing more spectral bands), hyper-temporally (faster sampling rates) and hyper-spatially (increasing number of smaller pixels). Accordingly, sensor technologies have outpaced transmission capa- bilities introducing a need to process more data at the sensor. While many sophisticated data processing capabilities are emerging, power and other hardware requirements for these approaches on conventional electronic systems place them out of context for resource constrained operational environments. To address these limitations, in this research effort we have investigated and char- acterized neural-inspired architectures to determine suitability for implementing RS algorithms In doing so, we have been able to highlight a 100x performance per watt improvement using neu- romorphic computing as well as developed an algorithmic architecture co-design and exploration capability.},
doi = {10.2172/1569155},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {9}
}