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FAJČÍK, M. SMRŽ, P. ZACHARIÁŠOVÁ, M.
Originální název
Automation of Processor Verification Using Recurrent Neural Networks
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
When considering simulation-based verification of processors, the current trend is to generate stimuli using pseudo-random generators (PRGs), apply them to the processor inputs and monitor the achieved coverage of its functionality in order to determine verification completeness. Stimuli can have different forms, for example, they can be represented by bit vectors applied to the input ports of the processor or by programs that are loaded directly into the program memory. In this paper, we propose a new technique dynamically altering constraints for PRG via recurrent neural network, which receives a coverage feedback from the simulation of design under verification. For the demonstration purposes we used processors provided by Codasip as their coverage state space is reasonably big and differs for various kinds of processors. Nevertheless, techniques presented in this paper are widely applicable. The results of experiments show that not only the coverage closure is achieved much sooner, but we are able to isolate a small set of stimuli with high coverage that can be used for running regression tests.
Klíčová slova
Functional Verification, Automation of Verification, Neural network, Recurrent Neural Network, Hopfield Net-work, UVM, Coverage-Driven Verification, Optimization Problem, Combinatorial Optimization
Autoři
FAJČÍK, M.; SMRŽ, P.; ZACHARIÁŠOVÁ, M.
Vydáno
27. 12. 2017
Nakladatel
Institute of Electrical and Electronics Engineers
Místo
Austin, Texas
ISBN
978-1-5386-3351-9
Kniha
18th International Workshop on Microprocessor and SOC Test, Security and Verification (MTV)
Strany od
15
Strany do
20
Strany počet
6
URL
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8396943
BibTex
@inproceedings{BUT154991, author="Martin {Fajčík} and Pavel {Smrž} and Marcela {Zachariášová}", title="Automation of Processor Verification Using Recurrent Neural Networks", booktitle="18th International Workshop on Microprocessor and SOC Test, Security and Verification (MTV)", year="2017", pages="15--20", publisher="Institute of Electrical and Electronics Engineers", address="Austin, Texas", doi="10.1109/MTV.2017.15", isbn="978-1-5386-3351-9", url="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8396943" }