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Item type:Publication, Access status: without files , Auto-generating ageing self-tests with hardware in the loop for commercial microcontrollersMicrocontrollers in critical systems require monitoring of hardware ageing to avoid unexpected malfunctioning. We previously introduced a deployable timing-based method with self-tests for ageing monitoring in commercial microcontrollers that relies solely on user-level information. While effective, manually constructing computing payloads for system-level tests can limit scaling and coverage of hardware-specific critical paths or subsystems. In this paper, we complement our technique with an automated generation of the self-test firmware. We explore several approaches for generation using hardware in the loop (HiL) evaluation on commercial microcontrollers, including a random generation and evolutionary strategies. In the evolutionary approach, we investigate two mutation mechanisms: random selection, and contextual bandits. Our evaluations on real hardware and a surrogate model show the impact of evaluation budget and algorithm selection on the effectiveness of automatic self-test generation. Results reveal that random generation is well suited to reduced HiL explorations, while evolutionary strategies benefit from extended evaluations. Our Hil-based technique is able to produce firmwares with up to double the degradation sensitivity as the functional tests provided by the vendor.Publicationtype: Conference Paper16 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Ageing monitoring for commercial microcontrollers based on timing windows(IEEE, 2026-04-27); ; ; Microcontrollers are increasingly present in embedded deployments and dependable systems, for which malfunctions due to hardware ageing can have severe impact. The lack of deployable techniques for ageing monitoring on these devices has spread the application of guard bands to prevent timing errors due to degradation. Applying this static technique can limit performance and lead to sudden failures as devices age. In this paper, we follow a software-based self-testing approach to design monitoring of hardware degradation for microcontrollers. Deployable in the field, our technique leverages timing windows of variable lengths to determine the maximum operational frequency of the devices. We empirically validate the method on real hardware and find that it consistently detects temperature-induced degradations in maximum operating frequency of up to 13.79% across devices for 60 °C temperature increase.Publicationtype: Conference Paper24 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Impact of seismic activities on the optical synchronization system of the European X-ray free-electron laser(Cambridge University Press (CUP), 2025-03-05); ; ; ; This study investigates the influence of seismic activities on the optical synchronization system of the European X-ray Free-Electron Laser. We analyze the controller I/O data of phase-locked-loops in length-stabilized links, focusing on the response to earthquakes, ocean-generated microseism and civilization noise. By comparing the controller data with external data, we were able to identify disturbances and their effects on the control signals. Our results show that seismic events influence the stability of the phase-locked loops. Even earthquakes that are approximately 5000 km away cause remarkable fluctuations in the in-loop control signals. Ocean-generated microseism in particular has an enormous influence on the in-loop control signals due to its constant presence. The optical synchronization system is so highly sensitive that it can even identify vibrations caused by civilization, such as road traffic or major events like concerts or sport events. The phase-locked loops manages to eliminate more than 99 % of the existing interference.Publicationtype: Journal Article23 37 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Reinforcement learning-trained optimisers and Bayesian optimisation for online particle accelerator tuning(Springer, 2024-12-01); ; ; ; Online tuning of particle accelerators is a complex optimisation problem that continues to require manual intervention by experienced human operators. Autonomous tuning is a rapidly expanding field of research, where learning-based methods like Bayesian optimisation (BO) hold great promise in improving plant performance and reducing tuning times. At the same time, reinforcement learning (RL) is a capable method of learning intelligent controllers, and recent work shows that RL can also be used to train domain-specialised optimisers in so-called reinforcement learning-trained optimisation (RLO). In parallel efforts, both algorithms have found successful adoption in particle accelerator tuning. Here we present a comparative case study, assessing the performance of both algorithms while providing a nuanced analysis of the merits and the practical challenges involved in deploying them to real-world facilities. Our results will help practitioners choose a suitable learning-based tuning algorithm for their tuning tasks, accelerating the adoption of autonomous tuning algorithms, ultimately improving the availability of particle accelerators and pushing their operational limits.Publicationtype: Journal Article33 119 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , A review of techniques for ageing detection and monitoring on embedded systems(Association for Computing Machinery (ACM), 2024-10-07); ; ; ; Embedded digital devices are progressively deployed in dependable or safety-critical systems. These devices undergo significant hardware ageing, particularly in harsh environments. This increases their likelihood of failure. It is crucial to understand ageing processes and to detect hardware degradation early for guaranteeing system dependability. In this survey, we review the core ageing mechanisms, and identify and categorize general working principles of ageing detection and monitoring techniques for Commercial-Off-The-Shelf (COTS) components that are prevalent in embedded systems: Field Programmable Gate Arrays (FPGAs), microcontrollers, Systems-on-Chips (SoCs), and their power supplies. From our review, we find that online techniques are more widely applied on FPGAs than on other components, and see a rising trend towards machine learning application for analysing hardware ageing. Based on the reviewed literature, we identify research opportunities and potential directions of interest in the field. With this work, we intend to facilitate future research by systematically presenting all main approaches in a concise way.Publicationtype: Journal Article25 61 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Studying the degradation of propagation delay on FPGAs at the European XFELAn increasing number of unhardened commercial-off-the-shelf embedded devices are deployed under harsh operating conditions and in highly-dependable systems. Due to the mechanisms of hardware degradation that affect these devices, ageing detection and monitoring are crucial to prevent critical failures. In this paper, we empirically study the propagation delay of 298 naturally-aged FPGA devices that are deployed in the European XFEL particle accelerator. Based on in-field measurements, we find that operational devices show significantly slower switching frequencies than unused chips, and that increased gamma and neutron radiation doses correlate with increased hardware degradation. Furthermore, we demonstrate the feasibility of developing machine learning models that estimate the switching frequencies of the devices based on historical and environmental data.Publicationtype: Conference Paper16 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Influence of environmental parameters on calibration drift in superconducting RF cavities(2024-01-01)5 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Learning to do or learning while doing: reinforcement learning and bayesian optimisation for online continuous tuning(2023-06-06); ; ; ; Online tuning of real-world plants is a complex optimisation problem that continues to require manual intervention by experienced human operators. Autonomous tuning is a rapidly expanding field of research, where learning-based methods, such as Reinforcement Learning-trained Optimisation (RLO) and Bayesian optimisation (BO), hold great promise for achieving outstanding plant performance and reducing tuning times. Which algorithm to choose in different scenarios, however, remains an open question. Here we present a comparative study using a routine task in a real particle accelerator as an example, showing that RLO generally outperforms BO, but is not always the best choice. Based on the study's results, we provide a clear set of criteria to guide the choice of algorithm for a given tuning task. These can ease the adoption of learning-based autonomous tuning solutions to the operation of complex real-world plants, ultimately improving the availability and pushing the limits of operability of these facilities, thereby enabling scientific and engineering advancements.Publicationtype: Preprint34 28 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Ageing analysis of embedded SRAM on a large-scale testbed using machine learning(IEEE, 2023); ; ; ; Ageing detection and failure prediction are essential in many Internet of Things (IoT) deployments, which operate huge quantities of embedded devices unattended in the field for years. In this paper, we present a large-scale empirical analysis of natural SRAM wear-out using 154 boards from a generalpurpose testbed. Starting from SRAM initialization bias, which each node can easily collect at startup, we apply various metrics for feature extraction and experiment with common machine learning methods to predict the age of operation for this node. Our findings indicate that even though ageing impacts are subtle, our indicators can well estimate usage times with an R2 score of 0.77 and a mean error of 24% using regressors, and with an Fl score above 0.6 for classifiers applying a six-months resolution.Publicationtype: Conference Paper18 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Anomaly detection based quench detection system for cw operation of srf cavities(2022-09-23); ; ; ; Superconducting radio frequency (SRF) cavities are used in modern particle accelerators to take advantage of their very high quality factor (Q). A higher Q means that a higher RF field can be sustained, and a higher acceleration can be produced in the cavity for length unity. However, in certain situations, e.g., too high RF field, the SRF cavities can experience quenches that risk creating damage due to the rapid increase in the heat load. This is especially negative in continuous wave (CW) operation due to the impossibility of the system to recover during the off-load period. The design goal of a quench-detection system is to protect the system without being a limiting factor during the operation. In this paper, we compare two different classification approaches for improving a quench detection system. We perform tests using traces recorded from LCLS-II and show that the ARSENAL classifier outperforms a CNN classifier in terms of accuracy.Publicationtype: Conference Paper24