Application of Formal Word Level Analysis to
Constrained Random Simulation 1st edtion by
Hyondeuk Kim, Hoonsang Jin, Kavita Ravi, Petr
Spacek, John Pierce, Bob Kurshan, Fabio Somenzi
ISBN 3540693888 9783540693888 pdf download
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A Numerical Abstract Domain Based on Expression Abstraction and Max
Operator with Application in Timing Analysis 1st edtion by Bhargav
Gulavani, Sumit Gulwani ISBN 3540693888 9783540693888
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Framework for Workflow Gridication of Genetic Algorithms in Java 1st
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,AutoMOTGen Automatic Model Oriented Test Generator for Embedded
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Analysis 1st edtion by Akash Lal, Thomas Reps ISBN 3540705437
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Ultrasonographic Anatomy of the Face and Neck for Minimally Invasive
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, Application of Formal Word-Level Analysis to
Constrained Random Simulation
Tool Paper
Hyondeuk Kim1,2 , Hoonsang Jin2 , Kavita Ravi2 , Petr Spacek2 , John Pierce2 ,
Bob Kurshan2 , and Fabio Somenzi1
1
University of Colorado at Boulder
2
Cadence Design Systems
Abstract. Constrained random simulation is supported by constraint solvers in-
tegrated within simulators. These constraint solvers need to be fast and memory
efficient to maintain simulation performance. Binary Decision Diagrams (BDDs)
have been successfully applied to represent constraints in this context. However,
BDDs are vulnerable to size explosions depending on the constraints they are rep-
resenting and the number of Boolean variables appearing in them. In this paper,
we present a word-level analysis tool DomRed to reduce the number of Boolean
variables required to represent constraints by reducing the domain of constraint
variables. DomRed employs static analysis techniques to obtain these reductions.
We present experimental results to illustrate the impact of this tool.
1 Introduction
Constrained random simulation is in increasing demand with hardware designers and
verification engineers. As the name indicates, it is the simulation of a design under
specified constraints. The user is required to capture the behavior of the environment
of the design as constraints and the simulation tools simulate the design under these
constraints with the aid of constraint solvers embedded in them. Commercial tools, such
as Specman, have been popular for providing this capability. To address the need for
constrained random simulation, modern hardware description languages (HDL), such
as System Verilog, have incorporated constraint specification as part of their syntax.
The overwhelming benefit of constrained random simulation over the traditional
writing of testbenches is the automation. Once the constraints are specified, the con-
straint solver in the simulator enumerates the valid scenarios instead of a human. Fur-
ther, by specifying weights on the search space, the user can indicate whether the con-
strained space should be sampled uniformly or specific areas should be focused on.
Given that constraint solving comprises the bulk of constrained random simulation
time, the efficiency and performance of constraint solvers is critical. Traditional con-
straint solving techniques, such as integer linear programming and constraint program-
ming, far lag the performance of simulators. Boolean engines, e.g., BDDs, have been
applied quite successfully to this problem[3] by taking advantage of the finite state na-
ture of HDL constraints. More recently, Kitchen and Keuhlmann[2] have provided a
A. Gupta and S. Malik (Eds.): CAV 2008, LNCS 5123, pp. 487–490, 2008.
c Springer-Verlag Berlin Heidelberg 2008
Constrained Random Simulation 1st edtion by
Hyondeuk Kim, Hoonsang Jin, Kavita Ravi, Petr
Spacek, John Pierce, Bob Kurshan, Fabio Somenzi
ISBN 3540693888 9783540693888 pdf download
https://ebookball.com/product/application-of-formal-word-level-
analysis-to-constrained-random-simulation-1st-edtion-by-hyondeuk-
kim-hoonsang-jin-kavita-ravi-petr-spacek-john-pierce-bob-kurshan-
fabio-somenzi-isbn-3540693888-9783540/
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at ebookball.com
, Get Your Digital Files Instantly: PDF, ePub, MOBI and More
Quick Digital Downloads: PDF, ePub, MOBI and Other Formats
A Numerical Abstract Domain Based on Expression Abstraction and Max
Operator with Application in Timing Analysis 1st edtion by Bhargav
Gulavani, Sumit Gulwani ISBN 3540693888 9783540693888
https://ebookball.com/product/a-numerical-abstract-domain-based-
on-expression-abstraction-and-max-operator-with-application-in-
timing-analysis-1st-edtion-by-bhargav-gulavani-sumit-gulwani-
isbn-3540693888-9783540693888-13066/
Assertion Based Verification Industry Myths to Realities Invited
Tutorial 1st edtion by Harry Foster ISBN 3540693888 9783540693888
https://ebookball.com/product/assertion-based-verification-
industry-myths-to-realities-invited-tutorial-1st-edtion-by-harry-
foster-isbn-3540693888-9783540693888-9514/
Conflict Tolerant Features 1st edtion by Deepak D’Souza, Madhu
Gopinathan ISBN 3540693888 9783540693888
https://ebookball.com/product/conflict-tolerant-features-1st-
edtion-by-deepak-daeurtmsouza-madhu-gopinathan-
isbn-3540693888-9783540693888-11224/
Framework for Workflow Gridication of Genetic Algorithms in Java 1st
edtion by Boro Jakimovski, Darko Cerepnalkoski, Goran Velinov ISBN
3540693888 9783540693888
https://ebookball.com/product/framework-for-workflow-gridication-
of-genetic-algorithms-in-java-1st-edtion-by-boro-jakimovski-
darko-cerepnalkoski-goran-velinov-
isbn-3540693888-9783540693888-12822/
,AutoMOTGen Automatic Model Oriented Test Generator for Embedded
Control Systems 1st edtion by Ambar Gadkari, Anand Yeolekar, Suresh,
Ramesh, Swarup Mohalik, Shashidhar ISBN 3540693888 9783540693888
https://ebookball.com/product/automotgen-automatic-model-
oriented-test-generator-for-embedded-control-systems-1st-edtion-
by-ambar-gadkari-anand-yeolekar-suresh-ramesh-swarup-mohalik-
shashidhar-isbn-3540693888-9783540693888-13928/
Computer Simulation of Materials at Atomic Level 1st Edition by Mark
Pederson, Peter Deák, Thomas Frauenheim ISBN 9783527603107 3527603
https://ebookball.com/product/computer-simulation-of-materials-
at-atomic-level-1st-edition-by-mark-pederson-peter-dea-k-thomas-
frauenheim-isbn-9783527603107-352760310x-15896/
Reducing Concurrent Analysis Under a Context Bound to Sequential
Analysis 1st edtion by Akash Lal, Thomas Reps ISBN 3540705437
9783540705437
https://ebookball.com/product/reducing-concurrent-analysis-under-
a-context-bound-to-sequential-analysis-1st-edtion-by-akash-lal-
thomas-reps-isbn-3540705437-9783540705437-14174/
Ultrasonographic Anatomy of the Face and Neck for Minimally Invasive
Procedures 1st edition by Hee Jin Kim, Kwan Hyun Youn, Ji Soo Kim, You
Soo Kim, Sung Ok Hong, Jongju Na ISBN 9811565597 9789811565595
https://ebookball.com/product/ultrasonographic-anatomy-of-the-
face-and-neck-for-minimally-invasive-procedures-1st-edition-by-
hee-jin-kim-kwan-hyun-youn-ji-soo-kim-you-soo-kim-sung-ok-hong-
jongju-na-isbn-9811565597-9789811565595-31/
orthodontics microimplants Complete technique step by step 1st
edition by Pablo Echarri, Tae Weon Kim, Lorenzo Favero, Hee Jin Kim,
OrTHoTaMiNe
https://ebookball.com/product/orthodontics-microimplants-
complete-technique-step-by-step-1st-edition-by-pablo-echarri-tae-
weon-kim-lorenzo-favero-hee-jin-kim-orthotamine-8158/
, Application of Formal Word-Level Analysis to
Constrained Random Simulation
Tool Paper
Hyondeuk Kim1,2 , Hoonsang Jin2 , Kavita Ravi2 , Petr Spacek2 , John Pierce2 ,
Bob Kurshan2 , and Fabio Somenzi1
1
University of Colorado at Boulder
2
Cadence Design Systems
Abstract. Constrained random simulation is supported by constraint solvers in-
tegrated within simulators. These constraint solvers need to be fast and memory
efficient to maintain simulation performance. Binary Decision Diagrams (BDDs)
have been successfully applied to represent constraints in this context. However,
BDDs are vulnerable to size explosions depending on the constraints they are rep-
resenting and the number of Boolean variables appearing in them. In this paper,
we present a word-level analysis tool DomRed to reduce the number of Boolean
variables required to represent constraints by reducing the domain of constraint
variables. DomRed employs static analysis techniques to obtain these reductions.
We present experimental results to illustrate the impact of this tool.
1 Introduction
Constrained random simulation is in increasing demand with hardware designers and
verification engineers. As the name indicates, it is the simulation of a design under
specified constraints. The user is required to capture the behavior of the environment
of the design as constraints and the simulation tools simulate the design under these
constraints with the aid of constraint solvers embedded in them. Commercial tools, such
as Specman, have been popular for providing this capability. To address the need for
constrained random simulation, modern hardware description languages (HDL), such
as System Verilog, have incorporated constraint specification as part of their syntax.
The overwhelming benefit of constrained random simulation over the traditional
writing of testbenches is the automation. Once the constraints are specified, the con-
straint solver in the simulator enumerates the valid scenarios instead of a human. Fur-
ther, by specifying weights on the search space, the user can indicate whether the con-
strained space should be sampled uniformly or specific areas should be focused on.
Given that constraint solving comprises the bulk of constrained random simulation
time, the efficiency and performance of constraint solvers is critical. Traditional con-
straint solving techniques, such as integer linear programming and constraint program-
ming, far lag the performance of simulators. Boolean engines, e.g., BDDs, have been
applied quite successfully to this problem[3] by taking advantage of the finite state na-
ture of HDL constraints. More recently, Kitchen and Keuhlmann[2] have provided a
A. Gupta and S. Malik (Eds.): CAV 2008, LNCS 5123, pp. 487–490, 2008.
c Springer-Verlag Berlin Heidelberg 2008
