Using DNNs and Smart Sampling for Coverage Closure Acceleration

Autor:	Eldad Haber, Avi Ziv, Raviv Gal
Rok vydání:	2020
Předmět:	Acceleration Functional verification Computer engineering Artificial neural network Computer science Event (computing) Closure (topology) Sampling (statistics) Solid modeling Data modeling
Zdroj:	MLCAD
DOI:	10.1145/3380446.3430627
Popis:	Coverage Directed Generation represents algorithms that are used to create tests or test-templates for hitting coverage events. Standard approaches for solving the problem use either user's intuition or random sampling. Recent work has been using optimization algorithms in order to hit, a single hard-to-hit event. In this work we extend the optimization technique for many events and show that by using a deep neural network one can accelerate the optimization significantly. The algorithms are presented on the NorthStar simulator where we show substantial improvement over random based techniques and a factor larger than 2 on other optimization-based techniques.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::54262eb6e8138d64fd430bd77d96cc51 https://doi.org/10.1145/3380446.3430627 Zobrazit plný text záznamu