1 PtHOMAS project, Ptolemy mini-conference '09 CR/RTC3-NA Elizabeth Latronico; UC Berkeley Jackie Mankit Leung | 4/16/2009 | © 2009 Robert Bosch LLC and

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PtHOMAS project, Ptolemy mini-conference '09

CR/RTC3-NA Elizabeth Latronico; UC Berkeley Jackie Mankit Leung | 4/16/2009 | © 2009 Robert Bosch LLC and affiliates. All rights reserved.

Ptolemy Hierarchical Orthogonal Multi-Attribute Solver (PtHOMAS)

Ptolemy mini-conference April 16 2009Jackie Mankit Leung, Thomas Huining Feng (UC Berkeley)Thomas Mandl, Elizabeth Latronico, Charles Shelton (Bosch)

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Motivation Cars are networked software systems

Up to 70 Electronic Control Units

Software crucial for many features Electronic stability control Parking assist Emissions control Engine Start/Stop Active and passive safety

How can we manage increasing complexity and interconnectedness?

Analysis approaches promising, but hand-annotation has drawbacks Time intensive to develop and maintain People are inconsistent, make errors Repeat for every composition

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How can a computer think like a software designer? Constant / Non-constant example

All sources are constant, so Display is constant (here, 21)

If any source is non-constant, Display could be non-constant

How does a person know that?1) How are constant and

non-constant related?

2) What are the rules for each individual actor?

3) What rules govern the connections among actors?

21? ?

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The ontology consists of two things: A property lattice, consisting of:

Property (“Concept”) Edge (“Relationship”)

Constraints on property-able objects (actors, expressions, …) Inequality terms Monotonic functions

Implementation Re-use Ptolemy type system constraint solving algorithms New! Graphical interface (for both lattice, inequality constraints) Java class definition for special cases



Frame the problem with an ontology

For example:

P(output) >= P(input)

PropertyEdge

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Property lattice example Complete partial ordering of

our properties

Common bottom element

Common top element

Can promote higher in lattice, but not lower

Type system is a familiar example Integer to Double is OK

3 to 3.0

Double to Integer is not! 3.14 to 3

General

Boolean

Not a Type

Integer

Double

Complex

Scalar

Long

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3.03.14

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Simplified Ptolemy type system

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Constant / Non-constant lattice How are constant and non-constant related?

Define a new lattice: Constant: Value is always the same Non-constant: Value might change

(but, perhaps does not) Correct but conservative

In this case, since there are no branches, Non-constant is the common top element

Can define your own lattice for your properties!

Constant

Non-Constant

Unknown

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Constraint analysis process Ptolemy has a scalable and efficient solver

algorithm Linear with the number of constraints Static analysis, so no execution required

Accepts a list of pairwise inequalities

Solves for least (what we want) or greatest upper bound

Constraint generation Need rules for each object

in library; sharing possible

Map objects to inequality terms (ports for most actors)

Set of rules for finite state machines and expressions

Constraint lista >= Constant

b >= ac >= a

Solver

Resulta, b, c == Constant

Ruleoutput >= input

Constraint listActor.out >= Actor.in1Actor.out >= Actor.in2

Actor2.out >= Actor2.in1

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Property constraints for actors in example Const actor:

output >= Constant

Sequence actor: output >= Non-constant

Math actors (AddSubtract, MultiplyDivide, Maximum) output >= input, for all input signals

output >= input1 output >= input2 Extends to multiple

channels for multiport

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Model-wide property constraints for connections Model-wide constraints are specified in new “solver box” actor

Here, sink >= source for connections Add1.input >= C1.output Add1.input >= C2.output Multiply3.input >= Add1.output

Add1.input

C2.output

C1.output

Add1.output

Multiply3.input

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Results for the example

Property annotation at every point in large models with minimal user effort

All constant sources One non-constant source

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Future topics Ontologies

New lattices for a variety of properties and use cases Learn about existing ontologies from different communities What ontology concepts are expressible in a lattice?

Model transformation Use property annotations to guide and simplify model transformation

Property correctness and extraction Verify hand-written rules Automatically extract rules for new objects Relate to partial evaluation / program specialization

For a more interesting demo, see us at the break!

Thanks! [email protected]

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Backup slides

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package ptolemy.data.properties.lattice.logicalAND;

public class Lattice extends PropertyLattice {

public Property TRUE = new True(this); public Property FALSE = new False(this); public Property UNKNOWN = new Unknown(this);

public Lattice() {

addNodeWeight(TRUE); addNodeWeight(FALSE); addNodeWeight(UNKNOWN);

addEdge(UNKNOWN, TRUE); addEdge(TRUE, FALSE);

if (!isLattice()) { throw new Exception("The PropertyLattice is not a lattice."); } }}

Lattice Java ClassThe package

defines the use-case / ontology for

this lattice.

Properties are defined as

public fields.

Relationship is specified by adding edges from

one element to another.

Documents

1 PtHOMAS project, Ptolemy mini-conference '09 CR/RTC3-NA Elizabeth Latronico; UC Berkeley Jackie Mankit Leung | 4/16/2009 | © 2009 Robert Bosch LLC and