1. Torino (Italy) – June 25th, 2013 Ant Colony Optimization for Mapping, Scheduling and Placing in Reconfigurable Systems Christian Pilato Fabrizio Ferrandi, PierLuca Lanzi, Christian Pilato, Donatella Sciuto Politecnico di Milano – Dip. di Elettronica, Informazione e Bioingegneria Antonino Tumeo Pacific Northwest National Laboratory – Richland, WA, U.S.A NASA/ESA Conference on Adaptive Hardware and Systems (AHS-2013) Torino, Italy – June 25-27, 2013

2. Christian Pilato – Politecnico di Milano, Italy Outline  Motivation  Related Work  Preliminaries and Motivation  Proposed Exploration Methodology  Experimental Results  Conclusions and Future Work 2

3. Christian Pilato – Politecnico di Milano, Italy Heterogeneous Systems  Mappingscheduling heterogeneous MPSoCs  Mapping and scheduling of partitioned applications are crucial in particular for heterogeneous MPSoCs design constraintsoverheads  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  Ant Colony Optimization is a promising constructive method to to produce very efficient solutions for the combined problem dynamic reconfiguration Considering FPGAs, possibility of introducing dynamic reconfiguration introduces several challenges to be taken into account 3

4. Christian Pilato – Politecnico di Milano, Italy Reconfigurable Systems  Partial Dynamic Reconfiguration  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 constraintsoverheads  Additional constraints and overheads are introduced reconfiguration latencies, number or reconfiguration ports and processing elements to drive the reconfiguration. placement  Accurate placement of the hardware components is critical Concurrent exploration of the design space for mapping, scheduling and placing of the tasks

5. Christian Pilato – Politecnico di Milano, Italy Related Work Exact solutions  [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 Ant Colony Optimization [Pilato et al. 2010] Ant Colony Optimization (ACO) has been demonstrated to produce good solutions, limiting the number of unfeasible ones Kernighan-Lin- Fiduccia-Matthesys  [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 5

6. Christian Pilato – Politecnico di Milano, Italy  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 placed Granularity and occupation for each task have to be defined in advance Target Architectural Template ARMARM DSPDSP Local Memory PowerPCPowerPC Shared Memory Shared bus 6 Cn C1C0 …

7. Christian Pilato – Politecnico di Milano, Italy Preliminaries: Problem Definition  Job  Job: generic activity (task or communication) to be completed in order to execute the specification  Implementation point  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  Mapping: assign each job to an admissible implementation point, respecting the architectural constraints (e.g., the limited resources of the components)  Scheduling  Scheduling: determine the order of execution of all the jobs of the specification in terms of priorities  Placing  Placing: determine the physical position of all the tasks that have to be executed in hardware  Objective  Objective: minimize the overall execution time of the application on the target architecture 7

8. Christian Pilato – Politecnico di Milano, Italy ACO Principles  Ant Colony Optimization (ACO)  Ant Colony Optimization (ACO) heuristic is a constructive approach that limits as much as possible the generation of unfeasible solutions Constructive approach Constructive approach, based on a decision tree, to generate parts of the solution based on the decisions taken in the previous parts. different combinations 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 Stochastic principles guarantee the exploration Heuristic principles and feed-backs Heuristic principles and feed-backs guarantee the exploitation of good parts of the solutions 8

9. Christian Pilato – Politecnico di Milano, Italy 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 9

10. Christian Pilato – Politecnico di Milano, Italy Stochastic Selection Process dj i  At each decision point d, the probability to assign a candidate job j to a proper implementation point i is:  Global information G  Global information G: feedback information Probability that the decision leads to a good solution  Local heuristic L  Local heuristic L: problem-specific hint “Adjusted” by the global heuristic if wrong i, j  Roulette wheel and extraction of a combination i, j The ant does not generate the probability if the decision leads to a constraint violation global heuristiclocal heuristic

11. Christian Pilato – Politecnico di Milano, Italy Decision Methods for Combined Problem reconfiguration and execution tasks  Scheduling can include both reconfiguration and execution tasks Executing tasks can be eligible only if the dependences are satisfied Reconfiguration tasks are always available (implicit hardware assignment) reconfiguration task  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

12. Christian Pilato – Politecnico di Milano, Italy Solution Evaluation for a Task Graph trace  The decisions performed by the ant give a trace Sequence of jobs, where each of them is assigned to an implementation point The 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 application Feedback to compare different solutions (reinforcement/penality of the global heuristic for the corresponding decisions) 12

13. Christian Pilato – Politecnico di Milano, Italy Experimental Setup  Target architecture  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 13

14. Christian Pilato – Politecnico di Milano, Italy Experimental Results  [9] corresponds to [Pilato et al. 2010]: ACO without support for PDR Great advantages in introducing PDR  [10] corresponds to [Banerjee et al. 2006]: KLFM with support for PDR ACO 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 14

15. Christian Pilato – Politecnico di Milano, Italy 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 efficient Constructive approach that limits unfeasible solutions Support 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 15

16. Christian Pilato – Politecnico di Milano, Italy ANY QUESTION? THANK YOU! christian.pilato@polimi.it 16