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An Architecture Exploration Framework for Embedded DSP Systems

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Title: An Architecture Exploration Framework for Embedded DSP Systems


1
An Architecture Exploration Framework for
Embedded DSP Systems
  • Presenter Ahmed Elhossini
  • ENG6530- Reconfigurable Computing
  • School of Engineering, University of Guelph

2
Outlines
  • Motivations Problem Definition
  • Background
  • Framework Overview
  • Searching the design Space
  • Evaluation Validation
  • Architecture Exploration Flow
  • Conclusions
  • Present Work

3
Motivations
  • The emerge of the concept of System-On-Chip
    introduced many challenges for the designer.
  • A tool that help the designer to make an
    estimation of the design requirements is required.

4
Problem Definition
  • Given a software application, explore the design
    space formed by the application and a core
    library to define
  • Hardware architecture
  • Computation (processors , cores, etc.)
  • Communication (Bus interfaces, FIFOs, etc.)
  • Partitioning/Mapping

5
Problem Definition
  • Find Mi, Ci , Bi j to meat Ai of Si
  • The problem is formulated as a multi-objective
    optimization problem.

6
Sample SOE Architecture
7
Architecture Exploration
8
Outlines
  • Motivations Problem Definition
  • Background
  • Framework Overview
  • Searching the design Space
  • Evaluation Validation
  • Architecture Exploration Flow
  • Conclusions
  • Present Work

9
Methodologies
  • Orthogonalization of concerns is an important
    concept in architecture exploration.
  • Separation of concerns deals with the following
  • Separating function and architecture.
  • Separating communication and computation.

10
The Y Chart
11
Requirements Challenges of Architecture
Exploration
  • Modeling
  • Application Modeling
  • Architecture Modeling
  • Mapping
  • Performance Measurement
  • System Update (Searching the Design Space)
  • Software
  • Architecture
  • Mapping

12
Architecture Exploration
13
Architecture Exploration Support
  • Both architecture and application need to be
    represented in some form to model different
    characteristics of both.
  • Different representations at different
    abstraction levels are used to model applications
    and architectures.
  • Co-simulation of both models is used to evaluate
    different implementations.

14
Architecture Modelling
  • Architecture Description Languages (ADL) are used
    to model architecture at a high level of
    abstraction.
  • Hardware Description Languages (HDL) are used to
    model the architecture at different levels of
    abstractions.
  • Instruction Set Models are used to model the
    behaviour of the processor.
  • When a micro-architecture templates is used as
    the implementation platform models and tools for
    simulation are available.

15
Application Modelling
  • Applications are usually specified in high level
    language. Compilers and interpreters are
    required.
  • Kahn Process Networks (KPN) model the application
    through concurrent processes communication using
    FIFO, unbounded, uni-directional, point to point
    channels.
  • Ptolemy framework provide a good environment to
    model different types of application with
    different computation models.
  • Directed Task Graph.

16
Evaluation Techniques
17
Evaluation Techniques
  • Accurate Simulation of the System
  • Give accurate evaluation with the cost of long
    evaluation time.
  • Trace Driven Simulation
  • An initial program run and extracts all memory
    accesses and store them in a trace which is used
    for performance estimation.
  • It is more efficient in the estimation of the
    performance of the memory sub-system.
  • Statistical Simulation
  • The statistical simulator does not execute the
    program in a precise order, but instead it
    simulate a statistical profile of the program.

18
Evaluation Techniques
  • Analytical Evaluation
  • Analytical model is developed for different
    components. This model are used for performance
    estimation.
  • The accuracy of analytical evaluation depends on
    the accuracy of the developed analytical model.
  • Intelligent Approaches
  • Some research is directed toward the use of
    intelligent approaches for the evaluation of
    embedded system.
  • Fuzzy Logic and Neural Networks are an example of
    such methods.

19
Searching the Design Space
20
Searching The Design Space
  • Exhaustive search covers all possible solutions
    in the design space.
  • It has the cost of long search time.
  • Local Search covers a portion of the design space
    to find a local sub-optimal solution.
  • Advantage short search time
  • Disadvantage high possibility of falling into
    local minima.

21
Mathematical Meta-Heuristics
  • A meta-heuristic approach based on genetic
    algorithms and particle swarm optimization was
    developed.
  • This approach is designed for multi-objective
    optimization.
  • This approach was verified using a set of
    multi-objective problems found in the literature.
  • Results was prepared for publication in
    evolutionary computation journal.

22
Outlines
  • Motivations Problem Definition
  • Background
  • Framework Overview
  • Searching the design Space
  • Evaluation Validation
  • Architecture Exploration Flow
  • Conclusions
  • Present Work

23
Framework Overview
24
Architecture Exploration Flow
  • Input Application Model
  • Output Near Optimal Hardware Architecture to
    implement the given application.
  • Target
  • Maximize the performance
  • Minimize the power consumption
  • Minimize the area (resources)
  • Maximize the flexibility

25
Application Model (Directed Task Graph)
26
Chromosome Representation
  • The candidate architecture is represented as a
    vector of integers.

27
Outlines
  • Motivations Problem Definition
  • Background
  • Framework Overview
  • Searching the design Space
  • Evaluation Validation
  • Architecture Exploration Flow
  • Conclusions
  • Present Work

28
Genetic Algorithms
  • A meta-heuristic approach.
  • Inspired by the natural selection and evolution
    of living species.
  • Based on three basic operations
  • Selection
  • Crossover
  • Mutation
  • Problem Representation - Individuals

29
Genetic Algorithms
30
Genetic Algorithms
  • Good exploration capabilities.
  • Exploiting capabilities are low.
  • The selection operation depends on the fitness of
    each individual.

31
Particle Swarm Optimization (PSO)
  • PSO is a robust stochastic optimization technique
    based on the movement and intelligence of swarms.
  • PSO applies the concept of social interaction to
    problem solving.
  • It was developed in 1995 by James Kennedy
    (social-psychologist) and Russell Eberhart
    (electrical engineer).
  • It uses a number of agents (particles) that
    constitute a swarm moving around in the search
    space looking for the best solution.
  • Each particle is treated as a point in a
    N-dimensional space which adjusts its flying
    according to its own flying experience as well as
    the flying experience of other particles.

32
Particle Swarm Optimization (PSO)
y
x
sk current searching point.

sk1 modified searching point.

vk current velocity.

vk1 modified
velocity.

vpbest velocity based on pbest.

vgbest velocity based on gbest
33
Initialization. Positions and velocities
http//www.cems.uwe.ac.uk/jsmith/ci/pso/PSO20min
i20tutorial.ppt
34
Neighbourhoods
geographical
social
http//www.cems.uwe.ac.uk/jsmith/ci/pso/PSO20min
i20tutorial.ppt
35
Particle Swarm Optimization
  • Good Exploiting Capabilities
  • Exploration of PSO depends on the particle
    movement parameters and the design space.
  • The selection of gbest, and pbest depends on the
    fitness of the different particles.

36
Hybrid GA-PSO
  • Combines the advantages of both PSO and GA.
  • Three Hybrid forms are introduced and tested.
  • A common representation was used for both
    algorithms.

37
Hybrid Representation
38
Hybrid GA-PSO-1
  • In this algorithm both PSO and GA operations are
    used in the same time.
  • This combination allows gaining the advantages of
    both algorithms.
  • PSO operations can be used to perform local
    search at each solution.

39
Hybrid GA-PSO-2
  • In this combination GA is used to explore the
    design space in the first phase of the search.
  • PSO is then used to exploit the solutions found
    by GA.

40
Hybrid GA-PSO-3
  • PSO is used for exploration.
  • GA is used to exploit the search.

41
Multi-objective optimization
  • Finding the optimum solution that meet different
    conflicting constraints, by varying different
    parameters.
  • Exact solution could be found by exhaustive
    search but this could be very time consuming.
  • Exhaustive search speeded up by the study of
    parameters dependency and clustering of
    parameters.

42
Multi-Objective Optimization (Pareto front)
  • Defines the set of solutions that covers all the
    trade-off of the different objectives.
  • A solution is said to be pareto optimal if it
    part of the pareto front.

43
Multi-objective optimization
  • Heuristic methods could be used to find a pareto
    optimal solution.
  • Several heuristic methods exist for
    multi-objective optimization.
  • Random Search Pareto (RSP).
  • Pareto Simulated Annealing (PSA).
  • Pareto Reactive Tabu Search (PRTS).
  • Genetic Algorithms.

44
Multi-objective Genetic Algorithms
  • Two widely used Multi-objective genetic
    algorithms
  • Strength Pareto Evolutionary Algorithm (SPEA).
  • Non-dominant Sorting Genetic Algorithm (NSGA).
  • SPEA uses a fine grained but and expensive
    computation schema.
  • NSGA uses a coarse grained and less expensive
    computation schema.

45
Multi-Objective Optimization
46
Strength Pareto Algorithm
  • Fitness value is assigned to each individual.
  • This value represent the strength of the
    individual in the Multi-Objective space
  • How many individuals it dominates.
  • The density of solutions near that individual.
  • External Archive is used to store the best
    individuals found so far.
  • Strength Pareto algorithm is used for both GA and
    PSO

47
Strength Pareto Algorithm
48
Testing The Meta-Heuristics Search Engine
  • Standard Benchmarks for Multi-objective
    optimization are used.
  • The search engine was also used to solve the
    scheduling problem (John Huisman)
  • Task Scheduler used to estimate the number of
    cycles required for a specific application.
  • The search engine used to search the design space
    for an optimal schedule.
  • The Task Scheduler is used for performance
    estimation

49
Scheduling for Embedded Systems
50
Testing The search Engine
51
Outlines
  • Motivations Problem Definition
  • Background
  • Framework Overview
  • Searching the Design Space
  • Evaluation Validation
  • Architecture Exploration Flow
  • Conclusions
  • Present Work

52
Core Library
  • The core library contains information about
  • processing cores
  • communication channels and buses
  • Components are defined using the following model

53
Performance Estimation
  • Performance estimation is the task of estimating
    the performance measures for a given
    architecture.
  • This estimation is based on the existence of
    different models of the cores forming up the
    architecture (power, performance , ext.)
  • Models provided by core vendors are based on
    different standards.
  • Each vendor provides a different estimation tool
    at a different level of abstraction.

54
Performance Estimation
55
Modeling of Embedded DSP cores
  • Each architecture is formed from a different set
    of cores and configurations.
  • Different benchmarks are available.
  • Each benchmark/architecture combination will be
    simulated (Cycle accurate/RTL) and performance
    measures will be extracted for each combination.
  • For each core its own performance measures for
    each architecture will be extracted.
  • For each core, the extracted performance measures
    will be used to create a model for that core.
  • ANN will be used to build the model from the
    extracted information.

56
Analytical Model
57
Modeling of Embedded DSP cores
58
Artificial neurons
Neurons work by processing information. They
receive and provide information in form of spikes.
x1 x2 x3 xn-1 xn
w1
Output
w2
Inputs
y
w3
.
.
.
wn-1
wn
The McCullogh-Pitts model
59
Artificial neurons
Nonlinear generalization of the McCullogh-Pitts
neuron
y is the neurons output, x is the vector of
inputs, and w is the vector of synaptic
weights. Examples
sigmoidal neuron Gaussian neuron
60
Artificial neural networks
Hidden Layer
Inputs Layer
Output Layer
An artificial neural network is composed of many
artificial neurons that are linked together
according to a specific network architecture. The
objective of the neural network is to transform
the inputs into meaningful outputs.
61
Neural network tasks
  • control
  • classification
  • prediction
  • approximation

These can be reformulated in general as FUNCTION
APPROXIMATION tasks.
Approximation given a set of values of a
function g(x) build a neural network that
approximates the g(x) values for any input x.
62
ANN Modeling Initial Results (Linear)
63
ANN Modeling Initial Results (Linear)
64
Modeling Example
65
Outlines
  • Motivations Problem Definition
  • Background
  • Framework Overview
  • Searching the Design Space
  • Evaluation Validation
  • Architecture Exploration Flow
  • Conclusions
  • Present Work

66
Architecture Exploration Flow
  • Manually create a call graph from profiling
    results.
  • This graph contains information about different
    software blocks.
  • It also contains the relation between different
    blocks.

67
VTune Profiling Results
68
Architecture Exploration Flow
  • Using a Graph Editor call graphs could be
    modified and user constraints could be added.
  • Graph information are stored into two different
    formats.

69
Graph Editor
70
Graph Editor
71
Architecture Exploration Flow
  • The Library Editor module enables adding,
    removing and modifying different components in
    the working library.
  • Library information are stored into two different
    files.

72
Library Editor
73
Architecture Exploration Flow
  • The GA-PSA modules used the graph information,
    core library, and a parameter file to search the
    design space of the problem.

74
Results File
75
Architecture Exploration Flow
  • The results obtained by searching the design
    space is decoded using the graph and library
    information.
  • Display Result modules is used to display the
    results.

76
Generate Different Results
77
Generate Different Results
78
Outlines
  • Motivations Problem Definition
  • Background
  • Framework Overview
  • Searching the Design Space
  • Evaluation Validation
  • Architecture Exploration Flow
  • Conclusions
  • Present Work

79
Conclusions
  • Architecture Exploration is Important with the
    new trends of Embedded Systems Design.
  • Many challenges face the designer during
    architecture exploration
  • Modeling of both architecture and application
  • Searching the design space
  • Evaluation of generated architectures.
  • Meta-Heuristic methods are efficient for
    searching the design space in architecture
    exploration.
  • Analytical and statistical methods for the
    evaluation of embedded systems give a good
    performance estimation in a reasonable time.

80
Conclusions
  • A framework is introduced for architecture
    exploration.
  • The flow starts with modeling a DSP application
    and ends with a set of architecture to implement
    the application.
  • The quality of the framework was verified as
    follows
  • The performance of the search engine was verified
    by testing known benchmarks that covers different
    level of complexities.
  • The use of ANN to model different cores ensures
    the accuracy of the performance estimation.

81
Conclusions
  • A hybrid PSO-GA algorithm based on the strength
    pareto algorithm is used to search the design
    space.
  • The evaluation of the solutions was improved
    using different techniques
  • The estimation of the performance is now based on
    the scheduling of tasks between different
    processors.
  • ANN based power estimator is being developed.
  • Analytical model is combined with the two
    previous techniques.
  • A GUI has been developed to enhance the
    user-framework interaction.

82
Thank You!!!
  • Questions ?
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