Efficient Sequence Generation for Hardware Verification Using Machine Learning

Gad, Muhammad; AboelMaged, Mostafa; Mashaly, Maggie; Ghany, Mohamed A. Abd El

doi:10.1109/icecs53924.2021.9665495

Cited by 1 publication

(2 citation statements)

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“…Once the state-action-state network is created for the entire DUT, by analyzing randomly generated simulations, the shortest path to reach any DUT state becomes available, and 100% state coverage can be achieved using a small number of actions. Compared to [ 12 ], where the proposed methodology focuses only on state coverage metrics, this current study can provide good results for any type of coverage that can be properly represented.…”

Section: Literature Reviewmentioning

confidence: 95%

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Cost-Efficient Approaches for Fulfillment of Functional Coverage during Verification of Digital Designs

2022

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Digital integrated circuits play an important role in the development of new information technologies and support Industry 4.0 from a hardware point of view. There is great pressure on electronics companies to reduce the time-to-market for product development as much as possible. The most time-consuming stage in hardware development is functional verification. As a result, many industry and academic stakeholders are investing in automating this crucial step in electronics production. The present work aims to automate the functional verification process by means of genetic algorithms that are used for generating the relevant input stimuli for full simulation of digital design behavior. Two important aspects are pursued throughout the current work: the implementation of genetic algorithms must be time-worthy compared to the application of the classical constrained-driven generation and the verification process must be implemented using tools accessible to a wide range of practitioners. It is demonstrated that for complex designs, functional verification powered by the use of genetic algorithms can go beyond the classical method of performing verification, which is based on constrained-random stimulus generation. The currently proposed methods were able to generate several sets of highly performing stimuli compared to the constraint-random stimulus generation method, in a ratio ranging from 57:1 to 205:1. The performance of the proposed approaches is comparable to that of the well-known NSGA-II and SPEA2 algorithms.

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Section: Literature Reviewmentioning

confidence: 95%

“…The authors of [ 12 ] present a way to automate coverage fulfillment in Finite State Machines (FSM). A machine learning model is trained with the actions required to reach a state starting from another DUT state.…”

Section: Literature Reviewmentioning

confidence: 99%