Learning microarchitectural behaviors to improve stimuli generation quality

Katz, Yoav; Rimon, Michal; Ziv, Avi; Shaked, Gai

doi:10.1145/2024724.2024914

Cited by 28 publications

(18 citation statements)

References 8 publications

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“…Functional verification starts with a verification plan, specifying the aspects of the design to verify [11]. In addition to manually-written direct tests, tests can be generated by constrained random test generation, which is guided by constraints and biases specified in a test bench (or test template).…”

Section: S-4mentioning

confidence: 99%

Data mining in functional test content optimization

Wang

2015

The 20th Asia and South Pacific Design Automation Conference

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This paper reviews the data mining methodologies [1]-[4] proposed for functional test content optimization where tests are sequences of instructions or transactions. Basic machine learning concepts and the key ideas of these methodologies are explained. Challenges for implementing these methodologies in practice are illustrated. Promises are demonstrated through experimental results based on industrial verification settings.

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Section: S-4mentioning

confidence: 99%

Data mining in functional test content optimization

Wang

2015

The 20th Asia and South Pacific Design Automation Conference

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“…[9,10,11]), constraints are formulated manually or randomly, which are expected to address the cases of a scenario. But since these cases are often not explicitly observable, the formulation can be tedious and the quality of the constraints strongly depends on the expertise of verification engineers.…”

Section: Related Workmentioning

confidence: 99%

“…[5,6,7,8,9,10,11,12,13]). This led to Coveragedriven Verification (CDV) which is well acknowledged and applied in industry.…”

Section: Introductionmentioning

confidence: 99%

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Determining Cases of Scenarios to Improve Coverage in Simulation-based Verification

Yang

Wille

Drechsler

2014

Proceedings of the 27th Symposium on Integrated Circuits and Systems Design

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Functional verification of complex designs is still dominated by simulation-based approaches. In particular, Coveragedriven Verification (CDV) is well acknowledged and applied in industry. Here, verification gaps in terms of inadequately checked scenarios are addressed and closed by generating and applying dedicated stimuli. In order to ensure a good coverage and, by this, a high verification quality, each scenario is supposed to become sufficiently triggered. However, the considered scenario may be triggered in several fashions and information about that is hardly available in the existing CDV approaches. In this work, we propose an approach which automatically derives this information. Examples and experimental evaluations illustrate how this improves coverage in simulation-based verification.

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“…In [1], Katz et al showed that micro-architectural information regarding instruction execution can help processor-level stimuli generators more easily reach verification goals. Coverage-driven test generation [4] is an approach to analyze coverage results dynamically and automatically adapt the test generation process to improve coverage.…”

Section: Related Workmentioning

confidence: 99%

Analyzing Efficacy of Constrained Test Program Generators - A Case Study

Kamath

Rahman

Wang

2013

2013 14th International Workshop on Microprocessor Test and Verification

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Functional verification of microprocessor designs exposes bugs in the design implementation by using a vast suite of randomly generated and directed test programs. Typically, more than one random test program generator(exerciser) is used. Our objective here is to develop a methodology to assess exerciser verification efficiency qualitatively and quantitatively. This understanding is used to identify untested design properties and fix coverage holes. We demonstrate this using a comparative analysis of the abilities of two in-house exercisers based to verify secure virtual mode(SVM) functionalities of an x86 instruction set architecture-based microprocessor core. We demonstrate that simulation data can be used to provide feedback on verification completeness to increase functional coverage.

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Learning microarchitectural behaviors to improve stimuli generation quality

Cited by 28 publications

References 8 publications

Data mining in functional test content optimization

Data mining in functional test content optimization

Determining Cases of Scenarios to Improve Coverage in Simulation-based Verification

Analyzing Efficacy of Constrained Test Program Generators - A Case Study

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