Presentation
AI-aided flow for digital verification of a multiprotocol SerDes PHY
DescriptionThis work describes the development and implementation of a verification flow that is aided by Artificial Intelligence (AI), supported by Synopsys VSO.ai. A comparative analysis was made between the conventional and the developed flow, on the verification of a multiprotocol SerDes PHY.
Coverage closure is fundamental in verification flows, despite being a resource-heavy task. Synopsys VSO.ai is a developing verification technology, based on AI, made to accelerate constraint-random and coverage-driven testbenches.
This is of special importance in automotive projects where the required high quality of coverage metrics demands a complete analysis of both code and functional coverage. Adding AI in the verification flow contributes to reducing Time To Results (TTR), to an easier identification of corner cases, to identify redundancies and missing coverage definition within the testbench and to the achievement of full coverage closure in a shorter time.
With the introduction of the AI-aided flow, a productivity boost was observed in comparison to the conventional regression flow, namely in reducing the required number of simulations and total regression time by a factor of 3 and 2, respectively. Such benefits provide the verification engineers increased flexibility in their allocation to debugging tasks.
Coverage closure is fundamental in verification flows, despite being a resource-heavy task. Synopsys VSO.ai is a developing verification technology, based on AI, made to accelerate constraint-random and coverage-driven testbenches.
This is of special importance in automotive projects where the required high quality of coverage metrics demands a complete analysis of both code and functional coverage. Adding AI in the verification flow contributes to reducing Time To Results (TTR), to an easier identification of corner cases, to identify redundancies and missing coverage definition within the testbench and to the achievement of full coverage closure in a shorter time.
With the introduction of the AI-aided flow, a productivity boost was observed in comparison to the conventional regression flow, namely in reducing the required number of simulations and total regression time by a factor of 3 and 2, respectively. Such benefits provide the verification engineers increased flexibility in their allocation to debugging tasks.
Event Type
Engineering Presentation
TimeMonday, June 232:45pm - 3:00pm PDT
Location2010, Level 2