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DTSTART:19700308T020000
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BEGIN:VEVENT
DTSTAMP:20211208T164413Z
LOCATION:Level 2 - Lobby
DTSTART;TZID=America/Los_Angeles:20211208T180000
DTEND;TZID=America/Los_Angeles:20211208T190000
UID:dac_DAC 2021_sess243_WIP149@linklings.com
SUMMARY:RTL regression test selection using Machine Learning
DESCRIPTION:Networking Reception, Work-in-Progress Poster\n\nRTL regressio
 n test selection using Machine Learning\n\nParthasarathy, Rushdi, Choudhar
 y, Nanda, Evans...\n\nWe address the problem of minimizing the validation 
 cost of iterative changes in RTL design cycles. Our approach learns charac
 teristics of both RTL code and tests during the verification process to ge
 nerate estimated likelihoods that a test will expose a latent bug introduc
 ed by incremental design modifications. This paper describes our online ma
 chine learning approach to the problem and its implementation. We also pre
 sent experiments on several real-world designs of various types with diffe
 rent types of test-suites that demonstrate significant time and resource s
 avings while maintaining validation quality.\n\nVirtual Program: Presented
  In-Person
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