NDLI: A PD-based methodology to enhance efficiency in testbenches with random stimulation

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A PD-based methodology to enhance efficiency in testbenches with random stimulation

Content Provider	ACM Digital Library
Author	Strum, Marius Marquez, Carlos Ivan Castro Chau, Wang Jiang
Abstract	Among the current verification techniques, functional verification has received important attention, since it represents an alternative that keeps HDL validation costs low throughout the circuit's design cycle. Functional verification is based in testbenches, and it works by exploring the whole (or relevant) model's functionality, applying sets of testcases. There are different techniques concerning testbenches operation, being the random stimulation an important approach, by which a huge number of testcases can be automatically created and applied. In testbench development, creation of random stimuli generators containing overlapping or invalid input parameter subspaces may occur, wasting computational time during testbench execution. In this work, we present a methodology to organize and apply random input stimuli by means of IP parameter domain, PD, formalism. The methodology provides the application of stimulus which: 1) belongs to the valid parameter subspace; 2) avoids repeated conditions; 3) covers the parameter subspaces uniformly. Moreover the methodology allows the automation of several tasks, making it efficient and less error-prone. Results on applying such a methodology are compared to cases where test vectors from the complete (unreduced, unorganized) verification space are generated manually, analyzing its relation to the coverage models specified for the design verification.
Starting Page	1
Ending Page	6
Page Count	6
File Format	PDF
ISBN	9781605587059
DOI	10.1145/1601896.1601921
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-08-31
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Design methodologies Parameter domains Coverage analysis System-on-chip Functional verification
Content Type	Text
Resource Type	Article

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