Kocik, Joseph Richard
(2021)
Space Station Power Forecasting with LSTMs on FPGAs.
Master's Thesis, University of Pittsburgh.
(Unpublished)
Abstract
Autonomous operations of space systems is an important and difficult task that will become even more imperative as space missions become increasingly remote. Accurate prediction of telemetry data can improve system monitoring and facilitate fault detection. This thesis presents a methodology for the acceleration of short-term forecasting of power data on an embedded platform designed for space. Initially, a long short-term memory (LSTM) network is trained to forecast voltage and current values from the International Space Station. This LSTM forecasts voltage and current minutes into the future while maintaining a low error rate. This LSTM network’s weights and biases are then used to create a new accelerated network which can be deployed on the FPGA of a Zynq-7045 system on a chip (SoC). The Zynq-7045 was selected because it is the same SoC used on the SHREC Space Processor, a space computer targeted for this study. A number of networks of varying sizes and history lengths are realized in hardware and evaluated against a software baseline. These networks were designed to be deployed on the resource-constrained FPGA fabric of the Zynq-7045 while maintaining the LSTM network architecture. The best performing LSTM networks were able to achieve over 3× speedup against a software baseline with minimal increase in forecasting error.
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Details
| Item Type: |
University of Pittsburgh ETD
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| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| Date: |
3 September 2021 |
| Date Type: |
Publication |
| Defense Date: |
15 July 2021 |
| Approval Date: |
3 September 2021 |
| Submission Date: |
5 July 2021 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
42 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Swanson School of Engineering > Electrical and Computer Engineering |
| Degree: |
MS - Master of Science |
| Thesis Type: |
Master's Thesis |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
LSTMs, Power Forecasting, FPGA Acceleration, Embedded Systems, Autonomous Monitoring |
| Date Deposited: |
03 Sep 2021 16:53 |
| Last Modified: |
03 Sep 2021 16:53 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/41428 |
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