Show simple item record

dc.contributor.authorVaradarajan, Aravind Krishnanen_US
dc.date.accessioned2020-03-29T06:00:38Z
dc.date.available2020-03-29T06:00:38Z
dc.date.issued2018-10-05
dc.identifier.othervt_gsexam:17195en_US
dc.identifier.urihttp://hdl.handle.net/10919/97506
dc.description.abstractIn this thesis, we provide solutions to two different bio-inspired algorithms. The first is enhancing the performance of bio-inspired test generation for circuits described in RTL Verilog, specifically for branch coverage. We seek to improve upon an existing framework, BEACON, in terms of performance. BEACON is an Ant Colony Optimization (ACO) based test generation framework. Similar to other ACO frameworks, BEACON also has a good scope in improving performance using parallel computing. We try to exploit the available parallelism using both multi-core Central Processing Units (CPUs) and Graphics Processing Units(GPUs). Using our new multithreaded approach we can reduce test generation time by a factor of 25�-- compared to the original implementation for a wide variety of circuits. We also provide a 2-dimensional factoring method for BEACON to improve available parallelism to yield some additional speedup. The second bio-inspired algorithm we address is for Deep Neural Networks. With the increasing prevalence of Neural Nets in artificial intelligence and mission-critical applications such as self-driving cars, questions arise about its reliability and robustness. We have developed a test-generation based technique and metric to evaluate the robustness of a Neural Nets outputs based on its sensitivity to its inputs. This is done by generating inputs which the neural nets find difficult to classify but at the same time is relatively apparent to human perception. We measure the degree of difficulty for generating such inputs to calculate our metric.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis item is protected by copyright and/or related rights. Some uses of this item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectRTLen_US
dc.subjectGPUen_US
dc.subjectNeural Netsen_US
dc.subjectRelaibilityen_US
dc.subjectPerformanceen_US
dc.subjectBranch Coverageen_US
dc.subjectTest Generationen_US
dc.subjectGenetic Algorithmen_US
dc.subjectCUDAen_US
dc.titleImproving Bio-Inspired Frameworksen_US
dc.typeThesisen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.description.degreeMSen_US
thesis.degree.nameMSen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineComputer Engineeringen_US
dc.contributor.committeechairHsiao, Michael S.en_US
dc.contributor.committeememberPatterson, Cameron D.en_US
dc.contributor.committeememberZeng, Haiboen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record