FunState – An Internal Design
Representation for Codesign
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A model that enables representations of
different types of system components.
Mixture of functional programming and state
machines.
Relationship to other models, regular state
machines, communicating FSMs, marked
graphs and synchronous dataflow, Boolean
and dynamic dataflow graphs, Petri Nets, and
SPI.
Issues of Complex Embedded
Systems
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Increasing heterogeneity and complexity,
decreasing expected time-to-market.
Encompasses a broad range of allocation,
scheduling, and binding issues in both
hardware and software.
SPI (System Property Intervals) is a
methodology designed to deal with certain
modeling problems.
Capture semantics of multiple input
languages.
What is FunState?
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Functions driven by State machines
Supports verification and representation of
different design decisions.
SPI and FunState are semantically equivalent.
Incremental implementation and verification.
Major results of FunState:
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Use as a internal representation of heterogeneous
embedded systems for scheduling and verification.
Explicit separation of control and dataflow can represent
many different models of computation.
FunStates as regular state machines allows many previously
investigated properties to be applied here.
Previous Fun Work
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Many different models of computations
including discrete-event, reactive, and
dataflow.
Codesign FSMs also combine synchronous
dataflow with FSMs
PSMs
Hierarchical FSMs introduced by Harel
including *charts and statecharts.
FunState attempts to reduce complexity by
modeling only characteristics of an input
specification which are relevant to a particular
design method.
Basics of FunState
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Basic components include a network, N, and
a FSM, M.
Network N contains (F, S, E)
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F is a set of functions
S is a set of storage units (either queues of
unbounded length or registers, linear arrays of
pairs of an address and a value)
E is a set of directed edges of (F X S) U (S X F)
Data is represented as valued tokens.
Datapath must be alternate between storage
units and functions.
Simple FunState Model
Functions
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Inputs and outputs have associated variables
that denote the number of consumed tokens
and number of produced tokens.
Can add or subtract nondeterministic (can be
bounded) number of tokens.
The State Machine
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Can use different models to specify the
FSM which controls the datapath.
Use a model based on ARGOS similar to
Harel’s statecharts.
Transitions are labeled with conditions
and actions. Predicates must be true,
then the transition is taken.
Bringing the Functions and
States Together
Initialize the current state of the state
machine to its initial value.
1.) Predicate Evaluation
2.) Check for progress
3.) State machine reaction
4.) Functions fire
Model Extensions
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The state machine can be a hierarchical
FSM with XOR and AND states.
The network may contain embedded
components.
This allows for arbitrarily deep
hierarchy.
Computational Model for
FunStates
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Use regular state machines for computation
purposes.
Assuming that there are no data
dependencies, hierarchy has been completely
flattened, and functions have constant rates
of consumption and production; FunState
models may be translated into static state
diagrams and dynamic sate diagrams.
Static and Dynamic State
Diagrams
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Static state diagram has a set of nodes, a set
of directed edges which have integer
distances between them, predicate functions,
and some initial state.
Dynamic state diagrams is an inifinite directed
graph which has transitions that may connect
nodes dynamically, with an initial state.
Relationship with
Communicating FSMs
FSM M1 can write to a queue during a
transition. M2 can guard its transition with
predicates. If transition is taken the token is
removed. POLIS models may also be
represented with this model.
Marked Graphs and
Synchronous Dataflow Graphs
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Nodes representing actors are connected with edges
representing communication queues.
A function determines the number of initial tokens in
queues, two functions denote the numbers of tokens
removed and added by firing of target and source
actors, respectively.
For FunState models, we can replace each actor with
a function and each edge with an edge-queue-edge.
There are existing functions for production and
consumption of tokens. State machine has one state
with a transition for every actor.
Cyclo-Static Dataflow Graphs
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Production and consumption rates of
actors change.
Boolean and Dynamic
Dataflow Graphs
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Introduce data-dependent datapath.
Differences with Petri Nets
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Queues in FunStates have a FIFO
behavior, unlike Colored Petri Nets
(CPNs)
No registers defined in CPN.
Transitions are controlled by a FSM,
whereas they are always ready in a
CPN.
Differences with SPI
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SPI does not explicitly separate control and
dataflow. States are not explicitly
represented.
The “state” of a SPI model is only the channel
contents.
SPI processes are autonomous like actors in a
dataflow model. FunState functions are all
controlled.
SPI autonomous systems can be modeled by
FunState models using local control.
The most important difference is the control
strategy.
Translating between SPI and
FunState
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Translate the FunState component with
a single state and several transitions.
Actions of these transitions trigger
functions corresponding to SPI process’s
modes.
Or we can model the state-dependent
behavior of the FunState by using
virtual feedback networks in SPI. Then
the SPI has a virtual state machine that
can control the mode tags.
Formal Verification
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FunStates can be verified with existing
techniques because of their representation in
regular state machines.
Interval diagram techniques can be used to
verify models efficiently and involves four
steps:
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Formulation of the verification goal with a computation tree
logic formula.
Representation of state transitions in the form of an interval
mapping diagram.
Represent state sets as interval decision diagrams
Apply boolean operations, quantification, and state set
mapping until a fixed point is reached.
Scheduling Policies
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Verification can assist development of
scheduling policies.
Static scheduling may use a simple
FSM.
Symbolic and ConflictDependent Scheduling
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Quasi-static scheduling may be modeled to
overcome the disadvantages of static or
dynamic schedule policies. Only datadependent choices are left to run-time
decisions.
Complex embedded systems have a growing
problem of multiple types of nondeterminism.
More complex models of computation
designed to handled these have their own
problems of system deadlocks and queue
overflows.
Conflict Dependent Scheduling
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Guaranteed to find a deadlock-free and
bounded solution if one exists.
This may be applied to a FunState model as a
refinement in the internal representation:
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Specify all possible schedules through a non-determinate
transition behavior; representing all design alternatives
Search state space for cycles representing valid schedules
Extracted schedule is made into a FSM and compacted using
state minimization techniques
Replace original specification part with the result, it may be
transformed into program code.
Example: Molecular Dynamics
Simulation
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Simple algorithm mapped onto a
main workstation (Host) and a
special purpose hardware
accelerator (CoProc).
Repeated computations in a
feedback loop distributed among
both processors.
Certain computations are unknown
at runtime, represented as
conflicts.
Dark circles represent alternatives
that may be chosen during
development.
Goal: Replace dark circles with
white circles, which represent
determinate solutions and only one
or completely disjoint transitions.
Conclusion
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FunState model allows internal
representation of complex
heterogeneous system behavior.
Can easily be used to relate other
models of computation.
Hierarchy and step-wise refinement can
be easily used to reduce nondeterminism.