TY  - JOUR
A1  - Laskov, Pavel
A1  - Gehl, Christian
A1  - Krüger, Stefan
A1  - Müller, Klaus-Robert
T1  - Incremental support vector learning: analysis, implementation and applications
JF  - Journal of machine learning research
N2  - Incremental Support Vector Machines (SVM) are instrumental in practical applications of online learning. This work focuses on the design and analysis of efficient incremental SVM learning, with the aim of providing a fast, numerically stable and robust implementation. A detailed analysis of convergence and of algorithmic complexity of incremental SVM learning is carried out. Based on this analysis, a new design of storage and numerical operations is proposed, which speeds up the training of an incremental SVM by a factor of 5 to 20. The performance of the new algorithm is demonstrated in two scenarios: learning with limited resources and active learning. Various applications of the algorithm, such as in drug discovery, online monitoring of industrial devices and and surveillance of network traffic, can be foreseen.
KW  - incremental SVM
KW  - online learning
KW  - drug discovery
KW  - intrusion detection
Y1  - 2006
SN  - 1532-4435
VL  - 7
SP  - 1909
EP  - 1936
PB  - MIT Press
CY  - Cambridge, Mass.
ER  - 
TY  - JOUR
A1  - Chen, Junchao
A1  - Lange, Thomas
A1  - Andjelkovic, Marko
A1  - Simevski, Aleksandar
A1  - Lu, Li
A1  - Krstic, Milos
T1  - Solar particle event and single event upset prediction from SRAM-based monitor and supervised machine learning
JF  - IEEE transactions on emerging topics in computing / IEEE Computer Society, Institute of Electrical and Electronics Engineers
N2  - The intensity of cosmic radiation may differ over five orders of magnitude within a few hours or days during the Solar Particle Events (SPEs), thus increasing for several orders of magnitude the probability of Single Event Upsets (SEUs) in space-borne electronic systems. Therefore, it is vital to enable the early detection of the SEU rate changes in order to ensure timely activation of dynamic radiation hardening measures. In this paper, an embedded approach for the prediction of SPEs and SRAM SEU rate is presented. The proposed solution combines the real-time SRAM-based SEU monitor, the offline-trained machine learning model and online learning algorithm for the prediction. With respect to the state-of-the-art, our solution brings the following benefits: (1) Use of existing on-chip data storage SRAM as a particle detector, thus minimizing the hardware and power overhead, (2) Prediction of SRAM SEU rate one hour in advance, with the fine-grained hourly tracking of SEU variations during SPEs as well as under normal conditions, (3) Online optimization of the prediction model for enhancing the prediction accuracy during run-time, (4) Negligible cost of hardware accelerator design for the implementation of selected machine learning model and online learning algorithm. The proposed design is intended for a highly dependable and self-adaptive multiprocessing system employed in space applications, allowing to trigger the radiation mitigation mechanisms before the onset of high radiation levels.
KW  - Machine learning
KW  - Single event upsets
KW  - Random access memory
KW  - monitoring
KW  - machine learning algorithms
KW  - predictive models
KW  - space missions
KW  - solar particle event
KW  - single event upset
KW  - machine learning
KW  - online learning
KW  - hardware accelerator
KW  - reliability
KW  - self-adaptive multiprocessing system
Y1  - 2022
U6  - https://doi.org/10.1109/TETC.2022.3147376
SN  - 2168-6750
VL  - 10
IS  - 2
SP  - 564
EP  - 580
PB  - Institute of Electrical and Electronics Engineers
CY  - [New York, NY]
ER  -