With　the　prevalence　of　big-data　technology,　intricate,　nanoscale　Multi-Processor　System-on-Chips　(MP-SoCs)　have　been　used　in　various　safety-critical　applications.　However,　with　no　extra　countermeasures　taken,　this　widespread　use　of　MP-SoCs　can　lead　to　an　undesirable　decrease　in　their　dependability.　This　study　presents　a　promising　approach　using　a　group　of　Embedded　Instruments　(EIs)　inside　a　processor　core　for　health　monitoring.　Multiple　health　monitoring　datasets　obtained　from　the　employed　EIs　are　sampled　and　collated　via　the　implemented　experiment　and　thereafter　used　for　conducting　its　remaining　useful　lifetime　prognostics.　This　enables　MP-SoCs　to　undertake　preventive　self-repair,　thus　realizing　a　zero　mean　downtime　system　and　ensuring　improved　dependability.　In　addition,　a　principal　component　analysis　based　algorithm　is　designed　for　realizing　the　EI　data　fusion.　Subsequently,　a　genetic　algorithm　based　degradation　optimization　is　employed　to　create　a　lifetime　prediction　model　with　respect　to　the　processor.　©　1996-2012　Tsinghua　University　Press.