Speaker
Description
The aim of this presentation is to assess the feasibility and on-board hardware performance requirements for on-board telemetry forecasting by implementing a Recurrent Neural Network(RNN)onlow-costmulticoreRISC-Vmicroprocessor.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 spacegrade 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. Index Terms—Deep Neural Networks, RISC-V, Space Systems, Artificial Intelligence
