Towards a Generic Design Space Exploration Framework

The set of all possible design alternatives for a system is referred to as a design-space, and design-space exploration (DSE) is the systematic exploration of the elements in a design-space. Various DSE techniques have been used for hardware/software co-design, configuration of software product lines and real-time software synthesis. Although at an abstract level DSE steps performed in these domains are similar, most of the current research is focused on domain specific frameworks which are tightly coupled with tools that evaluate point designs and use domain specific knowledge. There is a need for a generic tool that can be easily configured to model exploration problems from different domains as well on different levels of abstraction. In this paper we present Generic Design Space Exploration (GDSE) framework for domain independent DSE. This framework supports generic modeling of DSE problems from different domains using a language that allows a design-space to be encoded using domain-specific concepts and a simple constraint language that we designed. Rather than forcing the user to encode their design problem in a low-level constraint language, we advocate a higher level approach. The main contribution of this framework are: 1) it is able to support modeling of DSE in different domains 2) it supports a user-friendly constraint language that is expressive enough to specify constraints 3) the automated generation of low level constraint language code from the model alleviates the user from encoding the entire problem by hand 4) solver independence allows the user to experiment with different encoding

[1]  Viktor K. Prasanna,et al.  MILAN: A Model Based Integrated Simulation Framework for Design of Embedded Systems , 2001, OM '01.

[2]  Sandeep Neema,et al.  DesertFD: a finite-domain constraint based tool for design space exploration , 2010, Des. Autom. Embed. Syst..

[3]  Choudhury A. Rahman,et al.  UMHexagonS algorithm based motion estimation architecture for H.264/AVC , 2005, Fifth International Workshop on System-on-Chip for Real-Time Applications (IWSOC'05).

[4]  Alexander Schrijver,et al.  Theory of linear and integer programming , 1986, Wiley-Interscience series in discrete mathematics and optimization.

[5]  Gabor Karsai,et al.  Model-Integrated Computing , 1997, Computer.

[6]  Matthew J. Emerson,et al.  Techniques for Metamodel Composition , 2006 .

[7]  Rohit Saraswat,et al.  On the Use of DesertFD to Generate Custom Architectures for H.264 Motion Estimation , 2008, 15th Annual IEEE International Conference and Workshop on the Engineering of Computer Based Systems (ecbs 2008).

[8]  Mikolás Janota.  Do SAT Solvers Make Good Configurators? , 2008, SPLC.

[9]  Douglas C. Schmidt,et al.  Selecting highly optimal architectural feature sets with Filtered Cartesian Flattening , 2009, J. Syst. Softw..

[10]  Sandeep Neema,et al.  System-level synthesis of adaptive computing systems , 2001 .

[11]  Jürgen Teich,et al.  EXPLORA - Generic Design Space Exploration during Embedded System Synthesis , 2000, DIPES.

[12]  Gabor Karsai,et al.  Constraint-Based Design-Space Exploration and Model Synthesis , 2003, EMSOFT.

[13]  Peter J. Stuckey,et al.  Lazy Clause Generation Reengineered , 2009, CP.

[14]  Jaejoon Lee,et al.  FORM: A feature-;oriented reuse method with domain-;specific reference architectures , 1998, Ann. Softw. Eng..

[15]  Krzysztof R. Apt,et al.  Constraint logic programming using Eclipse , 2007 .

[16]  Luciano Lavagno,et al.  Metropolis: An Integrated Electronic System Design Environment , 2003, Computer.

[17]  Guido Tack,et al.  Constraint propagation: models, techniques, implementation , 2009 .

[18]  Peter J. Stuckey,et al.  MiniZinc: Towards a Standard CP Modelling Language , 2007, CP.

[19]  Jürgen Teich,et al.  System-Level Synthesis Using Evolutionary Algorithms , 1998, Des. Autom. Embed. Syst..

[20]  Douglas C. Schmidt,et al.  Automating Product-Line Variant Selection for Mobile Devices , 2007, 11th International Software Product Line Conference (SPLC 2007).

[21]  Peter J. Stuckey,et al.  Propagation via lazy clause generation , 2009, Constraints.

[22]  Aditya Agrawal.  Graph rewriting and transformation (GReAT): a solution for the model integrated computing (MIC) bottleneck , 2003, 18th IEEE International Conference on Automated Software Engineering, 2003. Proceedings..