Design and Synthesis of Image Processing Systems using Reconfigurable Dataflow Graphs Mainak Sen and...

Design and Synthesis of Image Processing Systems using Reconfigurable Dataflow Graphs

Mainak Sen and Shuvra S. Bhattacharyya

Department of Electrical and Computer Engineering, andInstitute for Advanced Computer Studies

University of Maryland at College Park

Maryland DSPCAD Research Grouphttp://www.ece.umd.edu/DSPCAD/home/dspcad.htm

November 22, 2005Leiden University, The Netherlands

Design and Synthesis of Image Processing Systems, 2University of Maryland at College Park

Outline

Dataflow-based model of computation for modeling the behavior of DSP applications

Decidable dataflow models Example: use of decidable dataflow as a model of computation for

modeling the mapping of (decidable) dataflow behaviors onto embedded multiprocessors

Structured reconfiguration of dataflow graphs Examples of meta-modeling techniques that can be classified as

structured, reconfigurable dataflow Parameterized dataflow and its application to SDF Homogeneous-parameterized dataflow and its application to SDF and

CSDF Experiments on a gesture recognition application

Summary


Dataflow-based design for DSP(Example from Agilent ADS tool)


DSP-oriented Dataflow Models of Computation

Used widely in design tools for DSP Application is modeled as a directed graph

Nodes (actors) represent functions Edges represent communication channels between functions Nodes produce and consume data from edges Edges buffer data in FIFO (first-in first-out) fashion

Data-driven execution model A node can execute whenever it has sufficient data on its input

edges The order in which nodes execute is not part of the specification The order is typically determined by the compiler, the hardware,

or both Iterative execution

Body of loop to be iterated a large or infinite number of times


Dataflow Features and Advantages

Exposes coarse-grain parallelism. Exposes high-level structure that facilitates analysis, verification,

and optimization. Captures multi-rate behavior. Complementary to ongoing advances in DSP compiler technology

for procedural languages, such as C and MATLAB. Encourages desirable software engineering practices: modularity

and code reuse Amenable also to aspect-oriented design.

Intuitive to DSP algorithm designers: signal flow graphs.


Evolution of Dataflow Models for DSP Synchronous dataflow: static multirate behavior

Agilent ADS, Cadence SPW, etc. Well-behaved dataflow: schemas for bounded dynamics Boolean/integer dataflow: Turing complete models Multidimensional synchronous dataflow: image and video Scalable synchronous dataflow: block processing

Synopsys COSSAP Cyclo-static dataflow: phased behavior

Synopsys El Greco, Eonic Systems Virtuoso Synchro, System Canvas

Bounded dynamic dataflow : bounded dynamics The processing graph method: reconfigurable dynamic DF

US Naval Research Laboratory, MCCI Autocoding Toolset Parameterized dataflow: dynamically-reconfigurable static DF Blocked dataflow: image and video in terms of reconfigurable dataflow


Modeling Design Space

Ex

pr e

ss

i ve

po

we

r

Verification / synthesis power

XC, BDF, DDF

XSDF

XCSDF

XCSDF, SSDFMDSDF,

WBDF

X

XPSDF

XPCSDF

(Third dimension: simplicity and intuitive appeal)


Decidable Dataflow Models

Modeling flow for representing static flowgraph behavior: Cyclo-static dataflow (CSDF), multiphase modeling Synchronous dataflow (SDF), multirate modeling Homogeneous synchronous dataflow (HSDF) Acyclic homogeneous synchronous dataflow (“task graphs”)

These are in decreasing order or generality Designs represented in the more general models can be converted

to equivalent representations in the less general ones e.g., CSDF SDF HSDF task graph

HSDF: each actor (graph node) produces/consumes exactly one data value to/from each incident output/input edge Suitable for exposing parallelism Not the best model for minimizing memory requirements


Synthesis Techniques for Decidable Models

Static scheduling: low overhead, predictability Performance analysis through synchronization graphs Loop scheduling

Implicit repetition in the dataflow graph (through changes in sample rate) needs to be translated into explicit repetition in the form of loops on the execution target.

Complex design space exists for such translation Complementary to procedural language techniques for nested loop

compilation Loop scheduling techniques

Simulation speedup (minimization of scheduling complexity) Code/data minimization Hierarchical parallel scheduling Block processing

Task scheduling for latency/throughput optimization Probabilistic design: exploiting tolerances to deadline misses


Example: Intermediate representations for synthesis from decidable dataflow models

Consider a decidable dataflow behavior that is to be implemented on a self-timed, embedded multiprocessor Natural way to implement DSP multiprocessors from decidable dataflow Actor assignment and ordering are performed statically Invocation (dispatch) of actors is performed dynamically, through

synchronization Candidate mappings of the behavior onto the architecture can be

represented through an intermediate representation that also has decidable dataflow semantics This representation is useful for understanding the performance,

communication overhead, and synchronization structure associated with the candidate mapping

Facilitates the separation of communication and synchronization functionality

This is a useful modeling methodology for design space exploration


Interprocessor Communication Graph (Gipc)

2r1

4s1

4s2

4s3

5s1

7r1

8r1

9r1

6

2

3

4

5

8

7

9

1

IPC GraphEvery edge (vi, vj) induces the

precedence constraint

2

4

1

3

6

5

8

7

9

Self-Timed Schedule

Proc 1: (1, 2, 3, 4, 6)

Proc 2: (5, 7, 8)

Proc 3: (9)

Proc 1 Proc 2 Proc 3

Self-timed schedule and its IPC graph


The synchronization graph Gs

Derived from the interprocessor communication graph

Synchronization edges are distinguished from interprocessor communication (IPC) edges

Synchronization edges represent precedence constraints that are enforced by synchronization protocols

IPC edges represent data transfers

Interprocessor connections

Coincident synchronization and IPC edges communication together with synchronization protocol (conventional approach)

IPC edge only communication without synch. protocol

Synchronization edge only synchronization protocol only


Applications of Synchronization Graphs

Simulation Throughput estimation through cycle mean analysis Removal of redundant synchronizations Resynchronization Conversion to more efficient synchronization protocols

(strongly connected synchronization graphs) Statically determining and minimizing the sizes of

interprocessor communication buffers• All are post-processing methods that can be applied to improve a wide range of existing task graph scheduling techniques on a wide range of multiprocessor architectures.

• These techniques benefit from good execution time estimates, but do not depend on exact execution time values to deliver useful results.


Beyond Decidable Models

Limited expressive power: DSP applications increasingly employ high-level dynamics in their behavior User interface functionality Mode changes Adaptive algorithms Reconfiguration of processing resources/parameters

However, key subsystems still exhibit large amounts of “quasi-static” structure --- structure that stays fixed across significant windows of time.

Various dynamic dataflow models have been proposed that address the limitation above by abandoning most or all restrictions related to decidable dataflow

However, these methods are correspondingly limited in their ability to exploit the quasi-static structure described above


Parameterized Dataflow: Structured Control of Dynamic Parameters

• The Key discipline that is imposed on reconfiguration is that each subsystem must have a consistent view of each of its actors (hierarchical or primitive) throughout any given iteration of that subsystem.


Parameterized Dataflow

Hierarchical modeling

subsystem

parent graph

subinit init

body

parameter n, ...

writes n

reads n

Parameterized DF subsystem is composed of 3 parmeterized DF graphs: init, subinit, body

Subsystem parameters configured in init/subinit,

used in body

Dynamically reconfigurable


Meta-modeling with parameterized dataflow

Parameterized dataflow can be applied to any dataflow model of computation (“base model”) to augment that model with dynamic reconfiguration capabilities in a structured way Provides for efficient quasi-static scheduling Enables execution to be viewed in terms of a sequence of

dataflow graphs in the base model Parameterized dataflow + XYZ “Parameterized XYZ” Examples of parameterized dataflow models of

computation that we are developing and experimenting with parameterized synchronous dataflow (PSDF) parameterized cyclo-static dataflow (PCSDF)


Parameterized Synchronous Dataflow (PSDF)

“Locally synchrony” conditions can be formulated and checked in a quasi-static fashion to ensure that bounded token production and consumption along with bounded delays lead to bounded memory requirements overall. This is not true of unstructured dynamic dataflow models, such

as general dynamic dataflow, boolean dataflow, and bounded dynamic dataflow

Techniques for construction of streamlined looped schedules for synchronous dataflow graphs have natural and efficient extensions to the construction of parameterized looped schedules for PSDF graphs.


PSDF Example: CD to DAT ConversioninitChild

setFac(sets i1,…d4)

CD

PF1

1 1 d1 i4

i1 i3 d2 d4

i2 d3PF2

preamble

PF3

PF4

DAT

params i1, d1, …., i4, d4

init

body

body

repeat 5 times { fire setFac /* sets i1, d1, i2, d2, i3, d3, i4, d4 */ int _g1 = gcd(i1, d2); int _g2=gcd((i2 x i1)/_g1, d3) int _g3=gcd((i3 x i2 x i1)/(_g2 x _g1), d4); repeat (d4/_g3) times { repeat (d3/_g2) times { repeat (d2/_g1) times { repeat (d1) times {fire CD} fire PF1 } repeat (i1/_g1) times {fire PF2} } repeat ((i2 x i1)/(_g2 x _g1)) times {fire PF3} } repeat ((i3 x i2 x i1)/(_g3 x _g2 x _g1)) times { fire PF4 } repeat (i4) times {fire DAT}}


PSDF Example: Speech Compression


PCSDF Version of Speech Compression


Outline

Dataflow-based model of computation for modeling the behavior of DSP applications

Decidable dataflow models Example: use of decidable dataflow as a model of computation for

modeling the mapping of (decidable) dataflow behaviors onto embedded multiprocessors

Structured reconfiguration of dataflow graphs Examples of meta-modeling techniques that can be classified as

structured, reconfigurable dataflow Parameterized dataflow and its application to SDF Homogeneous-parameterized dataflow and its application to SDF and


Summary


Homogeneous Parameterized Dataflow (HPDF)

• Parameterized dataflow model that can encapsulate dynamicity of application. • Meta-modeling technique. Hierarchical actors can have any other underlying dataflow model (SDF, CSDF, PSDF etc.)• Data production & consumption rates though dynamic are equal across an edge for a large number of applications - thus the name homogeneous.• Reconfiguration can be performed without introducing hierarchy when more natural to do so (advantage over parameterized dataflow).• Parameterized dataflow is a more powerful technique and thus can be used to represent a wider set of applications.


Applications

• Applications with dynamic run-time data and aggregated final-stage processes perform especially well for HPDF over SDF semantics.• Many applications in image and speech processing seem well suited for our model.• We applied the model on two applications – - A real-time video processing algorithm for smart camera developed at Princeton - A face detection algorithm developed at CFAR labs in UMD.


Application characteristics

A B M N

Dynamic but

balanced amount of

data Aggregatingfinal-stage

• This structure seems to be abundant in many audio/video applications.• Our HPDF model is a natural fit for applications with the above structure.


Gesture recognition algorithm

• Real-time video processing for gesture recognition. • Does low-level (red oval) and high-level processing.• Low-level processing recognizes body parts and identifies movements.• High-level processing recognized actions.• We concentrate on low-level processing.

Ref : W. Wolf, B. Ozer, T. LV. Smart cameras as embedded systems. IEEE Computer Magazine Vol 35, Iss 9, Sept 2002, Pages 48-53


HPDF model of gesture recognition algorithm

Region finding

Contour following

Ellipse Fitting

GraphMatching

Dynamic data Aggregating

final-stage

Dynamic data

n n p p

Ptolemy II implementation


Modeling with HPDF/CSDF

VIDEO INPUT

REGION EXTRACTION

CONTOURFOLLOWING

(s 1) (s 1)

(s 1) (s 1)

(s 1) (s 1)

(s 1) (Xi, Yi)

(s 1) (Xi, Yi)

ELLIPSE FITTING

(I 0,I ki ) (n

1)

MATCHp (pi1, qi 0)

p phases with 1 token and (n-p) phases with 0 token production

#phases = #pixels = s

p p and q n pi i


Integrating HPDF and CSDF

Number of phases in a fundamental period can vary dynamically. Number of tokens produced or consumed in a given phase can also

vary dynamically. HPDF constraint: the total number of tokens produced by a source

actor of a given edge in a given invocation (a fundamental period) must equal the total number of tokens consumed by the sink in its corresponding invocation.


Each frame has 384x240 pixels, so we model the input as a CSDF actor with 92160 = s phases.

Model captures pixel level parallelism present in Region. It also captures the frame level parallelism through the number of

phases in Input (s).

Finer granularity and Input modeling

VIDEO INPUT

REGION EXTRACTION

(s 1) (s 1)

(s 1) (s 1)

(s 1) (s 1)

#phases = #pixels = s


Modeling dynamicity - Contour

2 phases for Contour First one scans until finds a contour.

Output = 0 tokens Second one follows this contour and all the overlapping

ones. Output = ki tokens, each token is a list of pixels from a contour

Homogeneous condition remains: =s

i

ii YX )(


Scheduling

VRCEM (s V)(s R)(2I C)(n E)M (s VR)(2I C)(n E)M


• We applied HPDF to successfully model a face detection algorithm also.• We developed a TI DSP implementation of the HPDF model of the gesture recognition algorithm.• The application was run on a TMS320C64xx fixed point processor.• When implemented with our HPDF model, the runtime was 21405671 cycles. • With a 40ns cycle period, execution time for the application was 0.86 sec.

Results


Results (contd.)

• Scheduling overhead was minimal as imperatively highly streamlined quasi-static schedule was obtained.• Worst case buffer size 642 Kb when the input images were 384X240 pixels. HPDF modeling suggested buffer reuse between the edges.• Original C code had runtime of 27741882 cycles, execution time was 1.11 sec with the same clock period of 40 ns.• HPDF improved runtime by 23%.• Efficient hardware code generation is being looked into using hardware synthesis framework developed in our research group.


Summary

Dataflow-based model of computation for is attractive for modeling the behavior of DSP applications

Decidable dataflow models are useful for exposing and exploiting static structure in synthesis tools for DSP

Decidable dataflow models in conjunction with structured reconfigurable techniques allow for efficient handling of application dynamics

Examples of structured, reconfigurable dataflow techniques that we discussed: Parameterized dataflow and its application to SDF Homogeneous-parameterized dataflow and its application to SDF and


Other examples include dynamic configuration of graph topologies, and blocked dataflow modeling.


References

B. Bhattacharya and S. S. Bhattacharyya. Parameterized dataflow modeling for DSP systems. IEEE Transactions on Signal Processing, 49(10):2408-2421, October 2001

S. S. Bhattacharyya, R. Leupers, and P. Marwedel. Software synthesis and code generation for DSP. IEEE Transactions on Circuits and Systems --- II: Analog and Digital Signal Processing, 47(9):849-875, September 2000.

G. Bilsen, M. Engels, R. Lauwereins, and J. A. Peperstraete. Cyclo-static dataflow. IEEE Transactions on Signal Processing, 44(2):397-408, February 1996.

D. Ko and S. S. Bhattacharyya. Dynamic configuration of dataflow graph topology for DSP system design. In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, pages V-69-V-72, Philadelphia, Pennsylvania, March 2005.

E. A. Lee and D. G. Messerschmitt. Static scheduling of synchronous dataflow programs for digital signal processing. IEEE Transactions on Computers, February 1987.

S. Neuendorffer and E. Lee. Hierarchical reconfiguration of dataflow models. In Proceedings of the International Conference on Formal Methods and Models for Codesign, June 2004.

M. Sen, S. S. Bhattacharyya, T. Lv, and W. Wolf. Modeling image processing systems with homogeneous parameterized dataflow graphs. In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, pages V-133-V-136, Philadelphia, Pennsylvania, March 2005

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