Biblio

The advanced complex electronic systems increasingly demand safer and more secure hardware parts. Correspondingly, fault injection became a major verification milestone for both safety- and security-critical applications. However, fault injection campaigns for gate-level designs suffer from huge execution times. Therefore, designers need to apply early design evaluation techniques to reduce the execution time of fault injection campaigns. In this work, we propose a method to represent gate-level Single-Event Transient (SET) faults by multiple Single-Event Upset (SEU) faults at the Register-Transfer Level. Introduced approach is to identify true and false logic paths for each SET in the flip-flops' fan-in logic cones to obtain more accurate sets of flip-flops for multiple SEUs injections at RTL. Experimental results demonstrate the feasibility of the proposed method to successfully reduce the fault space and also its advantage with respect to state of the art. It was shown that the approach is able to reduce the fault space, and therefore the fault-injection effort, by up to tens to hundreds of times.

This paper studies the set similarity join problem with overlap constraints which, given two collections of sets and a constant c, finds all the set pairs in the datasets that share at least c common elements. This is a fundamental operation in many fields, such as information retrieval, data mining, and machine learning. The time complexity of all existing methods is O(n2) where n is the total size of all the sets. In this paper, we present a size-aware algorithm with the time complexity of O(n2-over 1 c k1 over 2c)=o(n2)+O(k), where k is the number of results. The size-aware algorithm divides all the sets into small and large ones based on their sizes and processes them separately. We can use existing methods to process the large sets and focus on the small sets in this paper. We develop several optimization heuristics for the small sets to improve the practical performance significantly. As the size boundary between the small sets and the large sets is crucial to the efficiency, we propose an effective size boundary selection algorithm to judiciously choose an appropriate size boundary, which works very well in practice. Experimental results on real-world datasets show that our methods achieve high performance and outperform the state-of-the-art approaches by up to an order of magnitude.