Ant Colony Optimization for Mapping, Scheduling and Placing in Reconfigurable Systems

Torino (Italy) – June 25th, 2013

Ant Colony Optimization for Mapping, Scheduling and Placing in Reconfigurable

Systems

Fabrizio Ferrandi, PierLuca Lanzi, Christian Pilato, Donatella SciutoPolitecnico di Milano – Dip. di Elettronica, Informazione e Bioingegneria

Antonino TumeoPacific Northwest National Laboratory – Richland, WA, U.S.A

NASA/ESA Conference on Adaptive Hardware and Systems (AHS-2013)

Torino, Italy – June 25-27, 2013

Christian Pilato – Politecnico di Milano, Italy

Outline

Motivation

Related Work

Preliminaries and Motivation

Proposed Exploration Methodology

Experimental Results

Conclusions and Future Work

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Heterogeneous Systems

Mapping and scheduling of partitioned applications are crucial in particular for heterogeneous MPSoCs

Different design constraints and overheads have to be necessarily considered to provide feasible and efficient solutions (e.g., limited area for hardware devices, interconnection topology, …)

Constructive methods are definitively required

Ant Colony Optimization is a promising constructive method toto produce very efficient solutions for the combined problem

Considering FPGAs, possibility of introducing dynamic reconfiguration introduces several challenges to be taken into account

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Reconfigurable Systems

Partial Dynamic Reconfiguration allows changing portion of FPGA configuration at run time

reuse of the device area to accelerate even more sections of an application

Additional constraints and overheads are introducedreconfiguration latencies, number or reconfiguration ports and processing elements to drive the reconfiguration.

Accurate placement of the hardware components is critical

Concurrent exploration of the design space for mapping, scheduling and placing of the tasks


Related Work

[Niemann and Marwedel 1997] Exact solutions for the combined problem with an ILP formulation on DAGs.

Different heuristic methods have been proposed to approach the problem[Pilato et al. 2010] Ant Colony Optimization (ACO) has been demonstrated to produce good solutions, limiting the number of unfeasible ones

[Banerjee et al. 2006] Optimization method based on Kernighan-Lin-Fiduccia-Matthesys (KLFM) adopts heuristics for the scheduling and the placing of the tasks

Reduced exploration in the design space

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Generic architectural template composed of processing and communication elements. A valid test case is the following one:

Number of pre-defined blocks where the tasks can be placedGranularity and occupation for each task have to be defined in advance

Target Architectural Template

ARM

DSP

Loca

l M

em

ory

Loca

l M

em

ory

Local Memory

PowerPC

Shared Memory

Sh

are

d b

us

6

CnC1C0 …


Preliminaries: Problem Definition

Job: generic activity (task or communication) to be completed in order to execute the specification

Implementation point: the mode for the execution of a job. It represents a combination of latency and requirements of resources on the related target component

Mapping: assign each job to an admissible implementation point, respecting the architectural constraints (e.g., the limited resources of the components)

Scheduling: determine the order of execution of all the jobs of the specification in terms of priorities

Placing: determine the physical position of all the tasks that have to be executed in hardware

Objective: minimize the overall execution time of the application on the target architecture

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ACO Principles

Ant Colony Optimization (ACO) heuristic is a constructive approach that limits as much as possible the generation of unfeasible solutions

Constructive approach, based on a decision tree, to generate parts of the solution based on the decisions taken in the previous parts.Analysis and evaluation of different combinations of mapping, scheduling and placing

Decision is based on a combination of local and global information, through a roulette wheel mechanism

Stochastic principles guarantee the explorationHeuristic principles and feed-backs guarantee the exploitation of good parts of the solutions

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Design Space Exploration with ACO

Initialize pheromones

Prepare N ants

Compute the set C of candidates

Perform a decision

Update set C of candidate

Evaluate design solution

Update pheromones

ACO

Colony

Ant

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Stochastic Selection Process

At each decision point d, the probability to assign a candidate job j to a proper implementation point i is:

Global information G: feedback informationProbability that the decision leads to a good solution

Local heuristic L: problem-specific hint“Adjusted” by the global heuristic if wrong

Roulette wheel and extraction of a combination i, jThe ant does not generate the probability if the decision leads to a constraint violation

nkijdijd

ijdijdijd

nknk LG

LGp

,,,,,

,,,,,, ][][

][][

global heuristic local heuristic


Decision Methods for Combined Problem

Scheduling can include both reconfiguration and execution tasksExecuting tasks can be eligible only if the dependences are satisfiedReconfiguration tasks are always available (implicit hardware assignment)

When a reconfiguration task is selected, it is generate a candidate choice also with respect to the position in the FPGA for its execution

The latency of reconfiguration tasks depends on where the task is assigned (i.e., if the reconfiguration takes effectively place or not)Scheduling of the reconfiguration takes into account also the availability of the reconfiguration port (ICAP) and the processor driving the reconfiguration


Solution Evaluation for a Task Graph

The decisions performed by the ant give a traceSequence of jobs, where each of them is assigned to an implementation pointThe position into the trace represents the priority for the scheduling (if they have been selected early, they have higher priority…)

List-based scheduler based on the trace (i.e., the implementation points and the priority values)

Different decisions performed by the ant correspond in exploring different design solutions (combination of mapping and scheduling)

Return overall execution time of the applicationFeedback to compare different solutions (reinforcement/penality of the global heuristic for the corresponding decisions)

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Experimental Setup

Target architecture composed of an ARM processor, a Digital Signal processor and an FPGA that also embeds a Power Pc processor

It allows to explore both hardware and software solutions

Synthetic benchmark to evaluate the scalability of the approach

We compared the ACO solutions with other search methods[Pilato et al. 2010] ACO where PDR is not supported: tasks can be allocated to the FPGA as long as they fit into the available area• Advantages of the PDR technique

[Banerjee et al. 2008] KLFM with support for PDR• Advantages of the ACO method

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Experimental Results

[9] corresponds to [Pilato et al. 2010]: ACO without support for PDRGreat advantages in introducing PDR

[10] corresponds to [Banerjee et al. 2006]: KLFM with support for PDRACO performs better in terms of quality of the solutions• Better exploration of the design space

Much more scalable• KLFM get stuck in approaching larger benchmarks

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Conclusions and Future Work

Ant Colony Optimization is very attractive to generate solutions for designing heterogeneous MPSoCs

Handling of design constraints is very simple and efficientConstructive approach that limits unfeasible solutionsSupport for different architectural templates can be easily provided

Results show that it is able to outperform most of the existing search methods

More robust and scalable

Future work:Closer integration with estimation methods and/or high-level synthesis for creating the implementations

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ANY QUESTION?

THANK [email protected]

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Ant Colony Optimization for Mapping, Scheduling and Placing in Reconfigurable Systems

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