Machine Learning for Space Applications on Embedded Systems
As space missions continue to increase in complexity, the operational capabilities and amount of gathered data demand ever more advanced systems. Currently, mission capabilities are often constrained by the link bandwidth as well as on-board processing capabilities. A large number of commands and complex ground station systems are required to allow spacecraft operations. Thus, methods to allow more efficient use of the bandwidth, computing capacity and increased autonomous capabilities are of strong research interest. Artificial Intelligence (AI), with its vast areas of application scenarios, allows for these challenges and more to be tackled in the spacecraft design. Particularly, the flexibility of Artificial Neural Networks as Machine Learning technology provides many possibilities. For example, Artificial Neural Networks can be used for object detection and classification tasks. Unfortunately, the execution of current Machine Learning algorithms consumes a large amount of power and memory resources. Additionally, the qualification of such algorithms remains challenging, which limits their possible applications in space systems. Thus, an increase in efficiency in all aspects is required to further enable these technologies for space applications. The optimisation of the algorithm for System on Chip (SoC) platforms allows it to benefit from the best of a generic processor and hardware acceleration. This increased complexity of the processing system shall allow broader and more flexible applications of these technologies with a minimum increase of power consumption. As Commercial off-the-shelf embedded systems are commonly used in NewSpace applications and such SoC are not yet available in a qualified manner, the deployment of Machine Learning algorithms on such devices has been evaluated. For deployment of machine learning on such devices, a Convolutional Neural Network model was optimised on a workstation. Then, the neural network is deployed with Xilinx's Vitis AI onto a SoC which includes a powerful generic processor as well as the hardware programming capabilities of an Field Programmable Gate Array (FPGA). This result was evaluated based on relevant performance and efficiency parameters and a summary is given in this thesis. Additionally, a tool utilising a different approach was developed. With a high-level synthesis tool the hardware description language of an accelerated linear algebra optimised network is created and directly deployed into FPGA logic. The implementation of this tool was started, and the proof of concept is presented. Furthermore, existing challenges with the auto-generated code are outlined and future steps to automate and improve the entire workflow are presented. As both workflows are very different and thus aim for different usage scenarios, both workflows are outlined and the benefits and disadvantages of both are outlined.
TinyML: From Basic to Advanced Applications
TinyML aims to implement machine learning (ML) applications on small, and lowpowered devices like microcontrollers. Typically, edge devices need to be connected to data centers in order to run ML applications. However, this approach is not possible in many scenarios, such as lack of connectivity. This project investigates the tools and techniques used in TinyML, the constraints of using low-powered devices, and the feasibility of implementing advanced machine learning applications on microcontrollers.
To test the feasibility of implementing ML applications on microcontrollers, three TinyML programs were developed. The first, a basic keyword spotting application able to recognize a set of words. The second, a program for training a neural network model on a microcontroller following an online learning approach. And the third, a federated learning program able to train a single global model with the aggregation of local models trained on multiple microcontrollers. The results show optimal performance in all three applications once deployed on microcontrollers. The development of basic TinyML applications is straightforward when the machine learning pipeline is understood. However, the development of advanced applications turned out to be very complex, as it requires a deep understanding of both machine learning and embedded systems.
These results prove the feasibility of successfully implementing advanced ML applications on microcontrollers, and thus, unveil a bright future for TinyML.
Machine Learning for Space Applications on Embedded Systems
As space missions continue to increase in complexity, the operational capabilities and amount of gathered data demand ever more advanced systems. Currently, mission capabilities are often constrained by the link bandwidth as well as on-board processing capabilities. A large number of commands and complex ground station systems are required to allow spacecraft operations. Thus, methods to allow more efficient use of the bandwidth, computing capacity and increased autonomous capabilities are of strong research interest. Artificial Intelligence (AI), with its vast areas of application scenarios, allows for these challenges and more to be tackled in the spacecraft design. Particularly, the flexibility of Artificial Neural Networks as Machine Learning technology provides many possibilities. For example, Artificial Neural Networks can be used for object detection and classification tasks. Unfortunately, the execution of current Machine Learning algorithms consumes a large amount of power and memory resources. Additionally, the qualification of such algorithms remains challenging, which limits their possible applications in space systems. Thus, an increase in efficiency in all aspects is required to further enable these technologies for space applications. The optimisation of the algorithm for System on Chip (SoC) platforms allows it to benefit from the best of a generic processor and hardware acceleration. This increased complexity of the processing system shall allow broader and more flexible applications of these technologies with a minimum increase of power consumption. As Commercial off-the-shelf embedded systems are commonly used in NewSpace applications and such SoC are not yet available in a qualified manner, the deployment of Machine Learning algorithms on such devices has been evaluated. For deployment of machine learning on such devices, a Convolutional Neural Network model was optimised on a workstation. Then, the neural network is deployed with Xilinx's Vitis AI onto a SoC which includes a powerful generic processor as well as the hardware programming capabilities of an Field Programmable Gate Array (FPGA). This result was evaluated based on relevant performance and efficiency parameters and a summary is given in this thesis. Additionally, a tool utilising a different approach was developed. With a high-level synthesis tool the hardware description language of an accelerated linear algebra optimised network is created and directly deployed into FPGA logic. The implementation of this tool was started, and the proof of concept is presented. Furthermore, existing challenges with the auto-generated code are outlined and future steps to automate and improve the entire workflow are presented. As both workflows are very different and thus aim for different usage scenarios, both workflows are outlined and the benefits and disadvantages of both are outlined.
TinyML: From Basic to Advanced Applications
TinyML aims to implement machine learning (ML) applications on small, and lowpowered devices like microcontrollers. Typically, edge devices need to be connected to data centers in order to run ML applications. However, this approach is not possible in many scenarios, such as lack of connectivity. This project investigates the tools and techniques used in TinyML, the constraints of using low-powered devices, and the feasibility of implementing advanced machine learning applications on microcontrollers.
To test the feasibility of implementing ML applications on microcontrollers, three TinyML programs were developed. The first, a basic keyword spotting application able to recognize a set of words. The second, a program for training a neural network model on a microcontroller following an online learning approach. And the third, a federated learning program able to train a single global model with the aggregation of local models trained on multiple microcontrollers. The results show optimal performance in all three applications once deployed on microcontrollers. The development of basic TinyML applications is straightforward when the machine learning pipeline is understood. However, the development of advanced applications turned out to be very complex, as it requires a deep understanding of both machine learning and embedded systems.
These results prove the feasibility of successfully implementing advanced ML applications on microcontrollers, and thus, unveil a bright future for TinyML.
