Name
Abstract | ||
|---|---|---|
Extensive software-based simulation continues to be the mainstream methodology for functional verification of designs. To optimize the use of limited simulation resources, coverage metrics are essential to guide the development of effective test suites. Traditional coverage metrics are defined based on either a functional model or a structural model of the design. If our goal is to select a subset of tests from a set of tests, using these coverage metrics require simulation of the entire set before the effectiveness of tests can be compared. In this paper, we propose a novel methodology that estimates the input space covered by a set of tests. We use unsupervised support vector analysis to learn such a space, resulting in a subset of tests that represent the original set of tests. A direct application of this methodology is to select tests before simulation in order to reduce simulation cycles. Consequently, simulation effectiveness can be improved. Experimental results based on application of the proposed methodology to the OpenSparc T1 processor are reported to demonstrate the practicality of our approach. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1145/1391469.1391536 | DAC |
Keywords | Field | DocType |
functional test selection,novel methodology,simulation effectiveness,proposed methodology,simulation cycle,entire set,mainstream methodology,extensive software-based simulation,unsupervised support vector analysis,original set,limited simulation resource,coverage metrics,algorithm design and analysis,functional testing,functional verification,logic design,unsupervised learning,symmetric matrices,support vector machines,functional analysis,hardware,kernel,functional model,random number generation,graphics,support vector,writing,encoding,computational modeling | Kernel (linear algebra),Permission,Functional verification,Algorithm design,Computer science,Support vector machine,Electronic engineering,Unsupervised learning,Software,Artificial intelligence,OpenSPARC,Machine learning | Conference |
ISSN | Citations | PageRank |
0738-100X | 17 | 0.81 |
References | Authors | |
11 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Onur Guzey | 1 | 42 | 4.41 |
| Li-C. Wang | 2 | 439 | 34.93 |
| Jeremy Levitt | 3 | 128 | 10.57 |
| Harry Foster | 4 | 98 | 9.24 |