{ "_id": "6a106526acfb0bcc41ca8aa8", "Package": "mergingTools", "Title": "Tools to Merge Hardware Event Monitors (HEMs) Coming from\nSeparate Subexperiments into One Single Dataframe", "Version": "1.0.1", "Authors@R": "person(given = \"Sergi\",\nfamily = \"Vilardell\",\nrole = c(\"aut\", \"cre\"),\nemail = \"sergi.vilardell@bsc.es\")", "Description": "Implementation of two tools to merge Hardware Event\nMonitors (HEMs) from different subexperiments. Hardware Reading\nand Merging (HRM), which uses order statistics to merge; and\nMUlti-Correlation HEM (MUCH) which merges using a multivariate\nnormal distribution. The reference paper for HRM is: S.\nVilardell, I. Serra, R. Santalla, E. Mezzetti, J. Abella and F.\nJ. Cazorla, \"HRM: Merging Hardware Event Monitors for Improved\nTiming Analysis of Complex MPSoCs,\" in IEEE Transactions on\nComputer-Aided Design of Integrated Circuits and Systems, vol.\n39, no. 11, pp. 3662-3673, Nov. 2020,\n. For MUCH: S. Vilardell, I.\nSerra, E. Mezzetti, J. Abella, and F. J. Cazorla. 2021. \"MUCH:\nexploiting pairwise hardware event monitor correlations for\nimproved timing analysis of complex MPSoCs\". In Proceedings of\nthe 36th Annual ACM Symposium on Applied Computing (SAC '21).\nAssociation for Computing Machinery.\n. 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