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Syst."],"published-print":{"date-parts":[[2014,3]]},"abstract":"<jats:p>\n            As manufacturing processes scale, designers are increasingly dependent on techniques to mitigate manufacturing defect and permanent failure. In embedded systems-on-chip, system lifetime and yield can be increased using\n            <jats:italic>slack<\/jats:italic>\n            \u2014under-utilization in execution and storage resources\u2014so that when components are defective, data and tasks can be remapped and rescheduled. For any given system, the design space of possible slack allocations is both large and complex, consisting of every possible way to replace each component in the initial system with another from the component library. Based on the observation that useful slack is often quantized, we have developed\n            <jats:italic>Critical Quantity Slack Allocation<\/jats:italic>\n            (CQSA), an approach that effectively and efficiently allocates execution and storage slack to jointly optimize system yield and cost. While exploring less than 1.4% of the slack allocation design space, our approach consistently outperforms alternative slack allocation techniques to find sets of designs within 1.4% of the lifetime-cost Pareto-optimal front. When applied to yield-cost optimization, our approach again outperforms alternative techniques, exploring less than 1.62% of the design space to find sets of designs within 4.27% of the yield-cost Pareto-optimal front. One advantage of managing failure at the system level is that the same techniques that improve lifetime often also improve yield. As a result, with little modification, CQSA is further able to perform effective joint optimization of lifetime and yield, finding designs within 1.6% of the Pareto-optimal front.\n          <\/jats:p>","DOI":"10.1145\/2535575","type":"journal-article","created":{"date-parts":[[2014,3,24]],"date-time":"2014-03-24T13:45:50Z","timestamp":1395668750000},"page":"1-33","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["Cost-effective lifetime and yield optimization for NoC-based MPSoCs"],"prefix":"10.1145","volume":"19","author":[{"given":"Brett H.","family":"Meyer","sequence":"first","affiliation":[{"name":"McGill University, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adam S.","family":"Hartman","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, PA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Donald E.","family":"Thomas","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, PA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2014,3,28]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVLSI.2003.817546"},{"key":"e_1_2_1_2_1","unstructured":"ARM. 2008. ARM Processors. http:\/\/www.arm.com\/products\/processors\/index.php.  ARM. 2008. ARM Processors. http:\/\/www.arm.com\/products\/processors\/index.php."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/MM.2005.110"},{"key":"e_1_2_1_4_1","doi-asserted-by":"crossref","unstructured":"P. Cyzak and A. Jaszkiewicz. 1998. Pareto simulated annealing\u2014A metaheuristic technique for multiple objective combinatorial optimization. J. Multi-Criteria Decision Anal. 7.  P. Cyzak and A. Jaszkiewicz. 1998. Pareto simulated annealing\u2014A metaheuristic technique for multiple objective combinatorial optimization. J. 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