Study of Simulink Discrete System MOC with Metropolis

1
Study of Simulink Discrete System MOC with
Metropolis

• Haiyang Zheng
• hyzheng_at_eecs.berkeley.edu
• Mentors
• Luciano Lavagno luciano_at_cadence.com
• Felice Balarin  felice_at_cadence.com

2
Outline

• Motivation and Introduction
• Simulink Discrete System MOC
• MOC Implementation in Metropolis
• A tool for automatic transformation of Simulink
  models into Metropolis models
• Simulation Demos
• Acknowledgement and Conclusion

3
Motivation

• Simulink MOC is a heterogeneous one. A
  non-trivial model contains both discrete and
  continuous models.
• A complex mixture model is usually hard to
  design, understand, and maintain.
• A better way is to use some simpler, better
  defined MOCs to model different parts of the
  complex system.

4
Introduction

• Discrete time points
• Continuous time intervals
• We choose purely discrete (sampled data) system
  as the study object.

5
Outline

• Motivation and Introduction
• Simulink Discrete System MOC
• MOC Implementation in Metropolis
• A tool for automatic transformation of Simulink
  models into Metropolis models
• Simulation Demos
• Acknowledgement and Conclusion

6
Discrete System in Simulink
B
A
C

• Each block in the block diagram has a sample
  time, the rate at which it executes during
  simulation.
• Multi-rate discrete systems contain blocks
  sampled at different rates.
• Simulator takes the simulation step as the
  fundamental sample time, the greatest common
  divisor of the systems actual sample times.

7
Outline

• Motivation and Introduction
• Simulink Discrete System MOC
• MOC Implementation in Metropolis
• A tool for automatic transformation of Simulink
  models into Metropolis models
• Simulation Demos
• Acknowledgement and Conclusion

8
Metropolis meta-model

• Netlist design of model (aggregation of objects
  and ports)
• Objects
• Process thread doing computation
• Medium
• Media for communication between processes
• State Media for communication between process and
  scheduler
• Scheduler defines policies to satisfy
  constraints
• Port Interface
• provides functions reference of other objects
• Constraint

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Discrete MOC in Metropolis I

• Processes communicate through channels, the
  medium.
• Each process has a period parameter, which is
  stored in the associated state medium.

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Discrete MOC in Metropolis II

• The scheduler calculates the schedule based on
  the sampled rates and invokes different processes
  periodically.
• The scheduler forces the finish of execution of
  processes to ensure the data precedence.
• The data dependency is not analyzed in the
  scheduler but in the model design phase.

11
Outline

• Motivation and Introduction
• Simulink Discrete System MOC
• MOC Implementation in Metropolis
• A tool for automatic transformation of Simulink
  models into Metropolis models
• Simulation Demos
• Acknowledgement and Conclusion

12
An Example Model
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Metropolis MMM Netlist

• public netlist simple
• public simple (String name)
• dtScheduler dtscheduler new
  dtScheduler("dtscheduler", 4)
• addcomponent (dtscheduler, this)
• RampProcess DiscreteRamp new
  RampProcess("DiscreteRamp", 0, 2)
• addcomponent(DiscreteRamp,this,"DiscreteRamp")
  
• dtStateMedium s0 new dtStateMedium("StateMed
  ium0", 0.25)
• addcomponent (s0, this)
• connect (DiscreteRamp,smport,s0)
• connect (dtscheduler, StateMedium0, s0)
• connect (dtscheduler, processPeriod0, s0)
• dtchannel c0 new dtchannel("c0")
• addcomponent(c0,this,"channel0")
• connect(DiscreteSubtract,outports0,c0)
• connect(Scope,inports0,c0)

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Comparison
An automatic transformation from the block
diagram representation to the text representation
is necessary.
15
A Tool for Transformation from Simulink models
into Metropolis models

• Transformation from Simlink Models into XML
  representations using MatlabUDM, a tool from
  Vanderbilt University.
• Transformation from XML representations into
  Metropolis MMM netlists with XSLT based tool.
• Two contributions
• It bridges the tools of Simulink and Metropolis
  with XML.
• It sorts the blocks based on data dependency
  analysis.

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Design of the Tool Data Dependency Analysis
Given order of blocks as BCAD.

• Conditions for processes to
• be ready to execute
• There is no input.
• All inputs are available.
• Algorithm
• Construct a status array with length as the
  number of processes and initiate it with O
  indicating the process not scheduled.
• Iterate the processes with given order, mark the
  ready process as Y, and schedule it. Repeat until
  all processes are marked and scheduled.

The sorted block order is ABCD.
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Outline

• Motivation and Introduction
• Simulink Discrete System MOC
• MOC Implementation in Metropolis
• A tool for automatic transformation of Simulink
  models into Metropolis models
• Simulation Demos
• Acknowledgement and Conclusion

18
Demos (Results)

• A Simulink Model
• Simulation result
• A Metropolis Model
• Simulation with code generated from SystemC

Result of Ramp is 1 Result of Ramp is 2 Result
of Gain is 4 Result of Subtract is 2 Outputis
2 Result of Ramp is 3 Result of Ramp is
4 Result of Gain is 8 Result of Subtract is
4 Outputis 4 Result of Ramp is 5 Result of Ramp
is 6 Result of Gain is 12 Result of Subtract
is 6 Outputis 6
19
Outline

• Motivation and Introduction
• Simulink Discrete System MOC
• MOC Implementation in Metropolis
• A tool for automatic transformation of Simulink
  models into Metropolis models
• Simulation Demos
• Acknowledgement and Conclusion

20
Acknowledgement Conclusion

• Thanks to
• Advice from Luciano Lavagno and Felice Balarin.
• Guang Yangs help on the C-code generation of
  Metropolis models with SystemC.
• ISIS of Vanderbilt University providing the
  MatlabUDM tool.
• A Discrete (Sampled Data) MOC is implemented in
  Metropolis.
• A transformation tool from Simulink XML models to
  Metropolis MMM netlists is implemented.