University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Rapid Prototyping of Deep Learning Models on Radiation Hardened CPUs

Blacker, P., Bridges, C. P. and Hadfield, S. (2019) Rapid Prototyping of Deep Learning Models on Radiation Hardened CPUs In: 13th NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2019), 22-24 Jul 2019, University of Essex, Wivenhoe Park, Colchester, UK.

Rapid Prototyping of Deep Learning Models on Radiation Hardened CPUs.pdf - Accepted version Manuscript

Download (1MB) | Preview


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 terrestrial 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 neural networks on terrestrial computers, however no radiation hardened equivalent parts are currently available. Field Programmable Gate Array devices are capable of efficiently implementing 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 framework 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.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Bridges, C.
Date : 22 June 2019
Funders : Airbus Defence and Space
Copyright Disclaimer : © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords : LEON; TensorFlow; On-board; Planetary rover; CNN; Deep learning; Autonomy
Related URLs :
Depositing User : Clive Harris
Date Deposited : 26 Jul 2019 07:26
Last Modified : 26 Jul 2019 07:28

Actions (login required)

View Item View Item


Downloads per month over past year

Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800