1. Design Space Exploration
Hardware/Software Codesign
Martin Holzer
Christian Doppler Laboratory for
Design Methodology of Signal Processing Algorithms
June 6th, 2008
Christian Doppler Laboratory for
Design Methodology of
Signal Processing Algorithms

2. Outline Embedded System Design Design Space Exploration Summary
System Characterization
Estimation of Design Properties
Multi-objective Optimization
Summary

3. Embedded System Design Flow
Specification model
Refinement
Implementation model
Increased design complexity
Design flow problems
Design productivity gap
Verification gap

4. Embedded System Design Flow
Specification model
Refinement
Implementation model
Increased design complexity
Design flow problems
Design productivity gap
Verification gap
Optimization objectives
Execution cycles
Silicon area
Power consumption 4 4

5. Motivation
Up to 90% of final development costs are determined in the first phase of the development

6. Design Space Exploration
Design space exploration is the process of investigating optimal implementation variants
Currently manual exploration
Design space not completely covered
Suboptimal solutions
Time consuming

7. Design Space Exploration
Characterization and analysis of algorithms
Estimation of implementation properties of the next level
execution cycles
area consumption
power consumption
Exploration of equally acceptable solutions

8. Metrics
A software metric is defined as numeric rating with the purpose to measure properties like
Complexity
Reliability
Length
Quality
Performance
Measurement procedure  numerical rating
Interpretation e.g. complexity
Methods
Dynamic
Static

9. Prediction of Implementation Effort
COCOMO
Lines of Code (LoC) dependent
Problem: prediction of source lines at the beginning of the project is hard
Function points
Based on
Number of inputs
Number of outputs
Internal functions
This measure can be derived from the specification itself

10. System Representation

11. Cyclomatic Complexity (V)
Minimal set of paths, where each edge is at least covered once.
Number of needed test cases in order to achieve full path coverage.
e = 8
v = 7
V = 8 – = 3

12. Degree of Parallelism High γ  high parallelism
DFG: faster computation
CFG: reuse capabilities

13. UMTS Slot Synchronization
Statistics
Operations
Control operations
Variables
Linguistic metric
Vocabulary
Structural metric
Parallelism
Tree height
Visualization
E.g. Kiviat chart

14. Execution Time Estimation
Metrics
Number of operations
Parallelism
Available resources

15. Execution Time Profile
Path analysis
Best Case Execution Time (BCET)
Worst Case Execution Time (WCET)
Infeasible paths
Condition (A < 1) && (A >= 1) cannot be fulfilled
BCET
WCET
Narrow bounds for the execution time interval

16. Area Time Trade-off (1)

17. Area Time Trade-off (2)

18. Multi-objective Optimization
Minimization of a set of conflicting functions
A decision x is Pareto optimal if there is no other decision that dominates x
Set of Pareto optimal points is called Pareto front
Evolutionary algorithm approach to compute Pareto front

19. Evolutionary Optimisation
area complexity
cycle count

20. Pareto Front Examples Control flow graph 10 basic blocks No loops
1010 design points
14 Pareto optimal design points
15 basic blocks
2 loops
1020 design points
29 Pareto optimal design points

21. Summary
Early design decision have high cost impact
System characterization with static properties
Estimation of implementation properties
Generation of design alternatives

22. Thank you for your attention.

23. Embedded System Design Flow

24. Metrics Linguistic Metrics Structural Metrics
Program Vocabulary e.g. S={ADD, SUB}
Memory Oriented Metrics
Control Oriented Metrics
Structural Metrics
Cyclomatic Complexity
Degree of Parallelism
Object Oriented Metrics
Depth of hierarchy

25. Parallelism of a DFG Nop = 8 Nopl = 5 γ = 8/5 = 1,6
Nop : Total number of operations
Nopl : Number of operations in the longest path
Nop = 8
Nopl = 5
γ = 8/5 = 1,6
High γ  high parallelism
faster computation
reuse capabilities
Nop : Total number of operations of type j
Noplj : Number of operations of type j in the longest path

26. Memory Orientation Metrics (MOM)
Nop : Number of operations
Nmac : Number of memory accesses
Ncop : Number of control operations
MOM  1 function is dominated by memory access
Nop = 8
Nmop = 4
MOM = 4/12 = 1/3

27. Control Orientation Metrics (COM)
Nop : Total number of operations
Ncop : Number of control statements
Nmac : Number of memory accesses
COM  1 function is dominated by control
Nop = 9
Ncop = 1
COM = 1/10 = 0,1

28. VP Generation