Study of Simulink Discrete System MOC with Metropolis Haiyang Zheng hyzheng eecs berkeley edu Mentors Luciano Lavagno luciano cadence com Felice Balarin felice cadence com 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 Motivation Simulink MOC is a heterogeneous one A nontrivial 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 Introduction Discrete time points t0 t1 x 4 y x 2 t2 t3 t Continuous time interval dy dt x 1 Discrete time points Continuous time intervals We choose purely discrete sampled data system as the study object 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 Discrete System in Simulink A B T 0 5 T 0 25 C T 0 75 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 system s actual sample times 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 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 Discrete MOC in Metropolis I Scheduler T 0 5 State Process A T 1 0 State Read Write State Medium Process B Channel Media Processes communicate through channels the medium Each process has a period parameter which is stored in the associated state medium Discrete MOC in Metropolis II A T 0 5 A P T T0 1 T0 0 5 B T 1 0 Scheduler AAB B P T T0 2 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 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 An Example Model T 1 0 2 1 4 3 T 1 0 T 1 0 T 0 5 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 StateMedium0 0 25 addcomponent s0 this connect DiscreteRamp smport s0 connect dtscheduler StateMedium 0 s0 connect dtscheduler processPeriod 0 s0 dtchannel c0 new dtchannel c0 addcomponent c0 this channel0 connect DiscreteSubtract outports 0 c0 connect Scope inports 0 c0 Comparison T 1 0 2 1 4 3 T 1 0 T 1 0 T 0 5 An automatic transformation from the block diagram representation to the text representation is necessary 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 Design of the Tool Data Dependency Analysis Given order of blocks as BCAD T 1 0 2 1 B A C D 4 3 T 1 0 OOOO B OOOO C OOOO A YOYO B OOYO D OOYO T 1 0 C A YYYO D YYYY T 0 5 YYYO Conditions for processes to be ready to execute The sorted block order is ABCD 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 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 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 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 Acknowledgement Conclusion Thanks to Advice from Luciano Lavagno and Felice Balarin Guang Yang s 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
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