On-Board Satellite Telemetry Forecasting with RNN on RISC-V Based Multicore Processor

Danilo Cappellone, S. Di Mascio, Gianluca Furano, A. Menicucci, Marco Ottavi

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

The aim of this paper is to assess the feasibility and on-board hardware performance requirements for on-board telemetry forecasting by implementing a Recurrent Neural Network (RNN) on low-cost multicore RISC-V microprocessor. Gravity field and steady-state Ocean Circulation Explorer (GOCE) public telemetry data was used for training RNNs with different hyperparameters and architectures. The prediction accuracy of these models was evaluated using mean error and R-squared score on the same test dataset. The implementation of the RNN on a RISC-V embedded device, representative of future space-grade hardware, required some adaptations and modifications due to the computational requirements and the large memory footprint. The algorithm was implemented to run in parallel on the 8 cores of the microprocessor and tiling was employed for the weight matrices. Further considerations have also been made for the approximation of sigmoid and hyperbolic tangent as activation functions.
Original languageEnglish
Title of host publication33rd IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems, DFT 2020
EditorsLuigi Dilillo, Mihalis Psarakis, Taniya Siddiqua
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages6
ISBN (Electronic)978-1-7281-9457-8
DOIs
Publication statusPublished - 2020
Event2020 33rd IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems - On-line Virtual Event
Duration: 19 Oct 202021 Oct 2020
Conference number: 33rd

Publication series

Name33rd IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems, DFT 2020

Conference

Conference2020 33rd IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems
Abbreviated titleDFT 2020
Period19/10/2021/10/20

Keywords

  • Deep Neural Networks
  • RISC-V
  • Space Systems
  • Artificial Intelligence

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