Achieve Faster Regression Throughput by Applying Machine Learning and other Advanced Technologies
Regressions time often becomes one of the biggest challenges to meet the tight project schedule with the increasing complexity of SoC designs. Verification engineers run a large number of random tests in regression to meet the coverage goals. This tutorial will cover methodologies, best practices and next generation simulation technologies to reduce both build and run time and there by achieving a significant improvement in the regression throughput.
Build time can be reduced easily using incremental and parallel build technologies, by partitioning the design and creating multiple build snapshots. Simulation run time can be reduced significantly by eliminating the time to run long routines of reset and initialization common across all the test cases.
Regression can be optimized easily by conventional verification management techniques. Further a significant jump in the throughput can be achieved by applying machine learning to analyze patterns hidden in the verification environment in order to optimize the regressions.
Join this Cadence tutorial to learn and apply machine learning and other advanced technologies in your regression flow to achieve significant improvement in the regression throughput. The tutorial will also present case studies based on our experience of working with customers to improve regression throughput using Cadence??XceliumTM?Logic Simulator.
DVCon India
Dec 14, 2021, 11:30 AM, Track-2
Tutorial 2.1: Achieve Faster Regression Throughput by Applying Machine Learning and Advanced Technologies
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