by Blacker, Peter, Bridges, Chris P. and Hadfield, Simon
Abstract:
Interest is increasing in the use of neural networks and deep-learning for on-board processing tasks in the space industry [1]. However development has lagged behind terres- trial applications for several reasons: space qualified computers have significantly less processing power than their terrestrial equivalents, reliability requirements are more stringent than the majority of applications deep-learning is being used for. The long requirements, design and qualification cycles in much of the space industry slows adoption of recent developments. GPUs are the first hardware choice for implementing neu- ral networks on terrestrial computers, however no radiation hardened equivalent parts are currently available. Field Pro- grammable Gate Array devices are capable of efficiently im- plementing neural networks and radiation hardened parts are available, however the process to deploy and validate an inference network is non-trivial and robust tools that automate the process are not available. We present an open source tool chain that can automatically deploy a trained inference network from the TensorFlow frame- work directly to the LEON 3, and an industrial case study of the design process used to train and optimise a deep-learning model for this processor. This does not directly change the three challenges described above however it greatly accelerates prototyping and analysis of neural network solutions, allowing these options to be more easily considered than is currently possible. Future improvements to the tools are identified along with a summary of some of the obstacles to using neural networks and potential solutions to these in the future.
Reference:
Rapid Prototyping of Deep Learning Models on Radiation Hardened CPUs (Blacker, Peter, Bridges, Chris P. and Hadfield, Simon), In Proceedings of the Conference on Adaptive Hardware and Systems (AHS), IEEE/NASA/ESA, 2019.
Bibtex Entry:
@InProceedings{Blacker19,
author = {Blacker, Peter and Bridges, Chris P. and Hadfield, Simon},
year = {2019},
month = {06},
pages = {},
title = {Rapid Prototyping of Deep Learning Models on Radiation Hardened CPUs},
Publisher = {IEEE/NASA/ESA},
Booktitle = {Proceedings of the Conference on Adaptive Hardware and Systems (AHS)},
Abstract = {Interest is increasing in the use of neural networks
and deep-learning for on-board processing tasks in the space
industry [1]. However development has lagged behind terres-
trial applications for several reasons: space qualified computers
have significantly less processing power than their terrestrial
equivalents, reliability requirements are more stringent than the
majority of applications deep-learning is being used for. The long
requirements, design and qualification cycles in much of the space
industry slows adoption of recent developments.
GPUs are the first hardware choice for implementing neu-
ral networks on terrestrial computers, however no radiation
hardened equivalent parts are currently available. Field Pro-
grammable Gate Array devices are capable of efficiently im-
plementing neural networks and radiation hardened parts are
available, however the process to deploy and validate an inference
network is non-trivial and robust tools that automate the process
are not available.
We present an open source tool chain that can automatically
deploy a trained inference network from the TensorFlow frame-
work directly to the LEON 3, and an industrial case study of
the design process used to train and optimise a deep-learning
model for this processor. This does not directly change the
three challenges described above however it greatly accelerates
prototyping and analysis of neural network solutions, allowing
these options to be more easily considered than is currently
possible.
Future improvements to the tools are identified along with a
summary of some of the obstacles to using neural networks and
potential solutions to these in the future.},
%Comment = {},
Url = {http://personalpages.surrey.ac.uk/s.hadfield/papers/Blacker19.pdf},
}