1. The GDSE Framework A Meta-Tool for Automated Design Space Exploration Tripti Saxena Graduate Student Vanderbilt University 1

2. Outline Background Motivation The Generic Design Space Exploration Framework – Reconfigurable Representation – Flexible Exploration Conclusion and Future Work Background Motivation The Generic Design Space Exploration Framework – Reconfigurable Representation – Flexible Exploration Conclusion and Future Work 2

3. Background x2x2 x1x1 design space Design Space product of possible discrete design choices e.g. selection of software components alternative hardware architectures selection of features Design Space Exploration find a design point Satisfies constraints Is “best” w.r.t. an objective function(s) 3

4. Background Software Product-line Engineering Face Recognition System Camera Image Compression PCA MahCosine Euclidean Bayesian ML MAP LDA IdaSoft Euclidean Face Recognition Algorithm [1..4] 4 Feature Model

5. Background Embedded Systems TDMA Priority EDF WFQ RISC DSP LookUp Cipher Application Mapping Hardware 5

6. Exploration Solver Exploration algorithm Exploration Solver Exploration algorithm Current DSE Frameworks -Configured to solve a particular DSE problem -Supports only ONE solver/solving technique, not efficient for every problem instance. Representation Design space Constraints Objectives 6 Examples of DSE frameworks FAMA Milan SPLOT PISA …

7. Motivation Hardware-Software Mapping Software Product Line configuration Web Server configuration SAT (e.g. Minisat) Mathematical Solver (e.g. LPSolve) Constraint Solver (e.g Gecode) Common core Reconfigurable Representation Multiple Solvers A reusable and flexible framework 7 Reusable Core

8. The Generic Design Space Exploration Framework DSML ADSEL Template eDSML Design Space Model Instance of FlatZinc Solver Solver Independent Constraint Problem in Minizinc Intermediate Language Intermediate Design Space Model Instance of FD Solver LP Solver Gecode Solver DESERT Model Transformation t GME 8 Common Core Reconfigurable Representation Minizinc Flexible Exploration

9. Generic Modeling Environment 9 Metamodeling Modeling MDE-based Framework Meta-Programmable Reuse of previously defined entities using libraries Translators for synthesis Done by Domain experts Done by Domain- engineers Enables reconfigurable representation

10. Minizinc 10 Medium Level Solver-Independent Language Express Combinatorial Search Problems Predefined translators for translation to different solver specific formats InterpreterModel Simplified Constraints + variables Constraint Solver (Flatzinc) Solutions LP Solve Minisat Solutions Enables flexible exploration

11. Overview of the GDSE Framework DSML ADSEL eDSML Design Space Model Instance of FlatZinc Solver Solver Independent Constraint Problem in Minizinc Intermediate Language Intermediate Design Space Model Instance of FD Solver LP Solver Gecode Solver DESERT 4 1 Model Transformation t 2 3 GME 11 Reuse existing DSML

12. Step 1: Domain Specific Modeling Language. Metamodel Entities Relationships Attributes Metamodel 12 Face Recognition Algorithm Model

13. DSE Problem: Face Recognition System DSML has to be extended to capture Design Space of possible variants DSE Properties Memory CPU Cost Constraints Bound constraint : Memory <= x Objective 13 PCA 1.Construct a Face Recognition System 2.Goal: Choose a face recognition algorithm from the variants satisfying selection + resource constraints Algo1 Algo2 Algo3 …

14. Step 2: Metamodel Composition DSML ADSEL eDSML Design Space Model Instance of GME FlatZinc Solver Solver Independent Constraint Problem in Minizinc Intermediate Language Intermediate Design Space Model Instance of FD Solver LP Solver Gecode Solver DESERT 4 1 Model Transformation t 2 3 GME 14 Performed by Domain- expert ONCE for a kind of DSE problem

15. The Abstract Design Space Exploration Language Template 15 Objective Design Space Tree COMPONENT TYPES CONSTRAINT TYPES OBJECTIVE TYPES All elements are abstract !

16. The ADSEL Component types PropertyType ValueType Domain Composition Function 16

17. The ADSEL Constraint and Objective types 17 e.g. A.Sel -> not B.Sel e.g. A.Memory <= 100e.g.minimize (cost) e.g. utilization

18. Metamodel Composition: Template Instantiation 18

19. Metamodel Composition: Template Instantiation 19

20. Composition Automation: eDSML Creator 20 GUI Semi-Automated Metamodel Creation based on user selection Written in C++

21. Step 3: Create Instance Model 21 NA M

22. Step 4: Perform DSE DSML ADSEL eDSML Design Space Model Instance of GME FlatZinc Solver Solver Independent Constraint Problem in Minizinc Intermediate Language Intermediate Design Space Model Instance of FD Solver LP Solver Gecode Solver DESERT 4 1 Model Transformation t 2 3 GME 22

23. Solutions 23 Solver Selection Solver

24. Summary – A Generic Framework Reusable Flexible – Case studies from different domains Software Product Line Configuration Architecture Synthesis Hardware Software Co-synthesis – Scalability: SPLE 24

25. Conclusion Other use cases – Hybrid Tool: Invoke multiple solvers in series – Scalability Analysis Tool Future Work – Wider range of case studies – Support parametric representation – Support algorithms for multi-objective optimization 25

26. Questions ? 26