Study of Simulink ™ Discrete System MOC with Metropolis

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 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.

Discrete time points x = 4, y = x - 2 t0 t1 t2 t3 t dy / dt = x + 1 Continuous time interval Introduction • Discrete time points • Continuous time intervals • We choose purely discrete (sampled data) system as the study object.

T = 0.5 T = 0.25 T = 0.75 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 system’s actual sample times.

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

Scheduler State Medium T = 0.5, State T = 1.0, State Process A Process B Read, Write Channel (Media) 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.

Scheduler P = T/T0 = 1 P = T/T0 = 2 A A T = 0.5 T = 1.0 T0 = 0.5 A A B B B 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.

T = 1.0 2 1 4 3 T = 1.0 T = 1.0 T = 0.5 An Example Model

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); …… } }

T = 1.0 2 1 4 3 T = 1.0 T = 1.0 T = 0.5 Comparison An automatic transformation from the block diagram representation to the text representation is necessary.

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. A Tool for Transformation from Simulink models into Metropolis models

A C B D OOOO OOOO OOOO T = 1.0 2 C D A C B 1 4 3 YOYO OOYO OOYO D B A YYYO YYYY (#) YYYO T = 1.0 T = 1.0 T = 0.5 Design of the Tool Data Dependency Analysis Given order of blocks as BCAD. The sorted block order is ABCD. • 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.

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

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.

Study of Simulink ™ Discrete System MOC with Metropolis