On efficient Monte Carlo-based Statistical Static Timing Analysis of digital circuits

Enhanced-Precision LHSMC of Electrical Circuit Considering Low Discrepancy

“…For optimization of effective dimensions, the initial value of each dimension was chosen by simulated annealing [18]. Now we describe the test cases and the experiments.…”

“…In [7,18], by controlling the initial values of the Sobol sequence, methods are suggested for optimizing the discrepancy of LDS. In our method, using [18], we optimize the discrepancy of the dimension paired using the Sobol sequence. We have a low runtime cost, since the number of dimensions that should be optimized is not large.…”

Correlation controlled sampling for efficient variability analysis of analog circuits

“…less than 20) [15]. In [10] a technique is proposed to improve the 2-D projection uniformity of Sobol samples. However, the confidence interval of the estimations which determines the required samples only becomes noticeably better than the traditional-MC when a very large number of samples (thousands) is used.…”

“…As a result, advanced sampling techniques such as, the stratified sampling, Latin Hypercube Sampling (LHS), and Quasi Monte Carlo (QMC), have been recently attracted the designers to achieve a faster convergence rate in MC-based timing analysis of digital circuits [9], [10]. A hybrid LHS-QMC SSTA method is proposed for the first time in [9], while a method is developed in [10] to generate low 2-D discrepancy QMC Sobol samples in order to further improve the convergence rate of the variance estimations. LHS method samples each dimension (process parameter) by partitioning its domain into equi-probable subranges, hence it improves the uniformity of the samples in one-dimensional projections.…”

Κυκλώματα Αριθμητικής Υπολοίπων Με Χαμηλή Κατανάλωση Και Ανοχή Σε Διακυμάνσεις Παραμέτρων

“…Για το λόγο αυτό η λύση που δόθηκε είναι η χρήση της στατιστικής ανάλυσης της καθυστέρησης SSTA (statistical time analysis) για την επίτευξη υψηλής τιµής για την απόδοση χρονισµού Ι. Κουρέτας ◭ ♦ ◮ και για αξιόπιστη ανάλυση και ϐελτίωση των κυκλωµάτων. Την τελευταία δεκαετία για την αντιµετώπιση της διακύµανσης των παραµέτρων έχουν προταθεί πολλές µέθοδοι SSTA [87,88,[88][89][90][91][92][93][94][95][96][97][98][99][100][101][102]. Σύµφωνα µε την SSTA οι καθυστερήσεις αντιµετωπίζονται ως τυχαίες µεταβλητές οι οποίες χαρακτηρίζονται από συγκεκριµένη συνάρτηση πυκνότητας πιθανότητας (PDF).…”