Design and Implementation of Adaptive Signal - PowerPoint Presentation

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Design and Implementation of Adaptive SignalProcessing Systems Using Markov DecisionProcesses

1. Georgia Institute of Technology, USA2. University of Maryland, USA3. Tampere University of Technology, Finland

With contributions from: Lin Li2, Adrian E. Sapio2, Jiahao Wu2, Yanzhou Liu2, Kyunghun Lee2, and Shuvra S. Bhattacharyya2,3

Marilyn

Wolf

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Outline

2Motivation and ContributionBackgroundFramework for Design and Implementation of Adaptive Signal Processing SystemsCase Study of Channelizer/receiver ApplicationConclusionsSlide3

Motivation and Contribution

3Modern signal processing applications impose increasing demands of:AdaptivityEfficiencyReconfigurabilityFlexibilityChallenges at many levels of system design, implementation and optimization:Dynamically-changing working scenariosStringent constraints on energy-efficiency and real-time performanceMultidimensional design spaceSlide4

Motivation and Contribution

4This research aims at developing a framework for design and implementation of adaptive embedded signal processing systems that integratesautomated, MDP-based generation of optimal reconfiguration policiesdataflow-based application modelingimplementation of embedded control software that carries out the generated reconfiguration policiesSlide5

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Background Markov Decision Processes (MDPs)

Probabilistic transitions combined with inputs.Given an input at a state, next state is chosen probabilistically.A policy p defines the actions in each state s.Optimal policy maximizes rewards.Slide6

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Basic concepts of dataflow modeling:Digital Signal Processing (DSP) system directed dataflow graphComputational functions nodes (actors)Communication channels between actors edges (FIFOs)Actor Firing: Actor execution as a discrete unit of computationToken: The encapsulation of some well-defined amount of data.Consumption/Production Rate: Number of tokens consumed/produced from/to the input/output FIFO during one actor firing.

Background

Dataflow

ModelingSlide7

BackgroundLightWeight DataFlow (LWDF)

7LightWeight DataFlow (LWDF): a programming methodology for integration, experimentation, and optimization with dataflow modeling approaches.Actor Mode: Determines the dataflow behavior of the actor.Enable function: Checks actor firing condition according to its current mode. This function can be bypassed at run time if static scheduling analysis can ensure the result.

Invoke function: Executes an actor firing according to its current mode.Lightweight Dataflow Environment (LIDE): Provides a compact set of application programming interfaces (APIs) that is used for constructing, connecting, and executing dataflow components such as actors, edges, and graphs.LIDE APIs have been implemented in a variety of implementation languages, including C, Verilog, and CUDASlide8

Framework for Adaptive Signal Processing Systems

8We propose a novel framework, called

Hierarchical MDP framework for Compact System-level Modeling (HMCSM).Slide9

Framework for Adaptive Signal Processing Systems

9Hierarchical MDP SubsystemA single Markov decision process (MDP) is transformed into a hierarchy of multiple MDPs that can be independently solved.Such decomposition into a collection of simpler MDPs leads to more efficient design optimization.The stochastic models of the environment and system include, for each of the environment and system, the definition of the state space and the state transition matrix (STM). Control Actions represent the set of possible reconfiguration operations.

The Reward Function maps state-action pairs into scores that assess the utility of performing the associated control action during the given state.Slide10

Framework for Adaptive Signal Processing Systems

10Parameterized LIDE ImplementationDataflow graph implementation of the application developed using LIDEParameter updates are made by setting (at design time or run time) appropriate variables in this implementationConfiguration Control Machine (CCM)Determines, based on the current environmental state and system state, whether or not to perform a dynamic reconfiguration operationDetermines the specific reconfiguration operation that is to be applied to the system if reconfiguration is to be performed

Policy Mapping Engine (MPE)Translates control actions into updates to dynamic parameters in the embedded softwareSlide11

Case Study of Channelizer/receiver Application (1)

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Case Study of Channelizer/receiver Application (2)

12 PSDF specification of channelizer subsystem.

Init Graph: modeling construct in parameterized synchronous dataflow (PSDF) for reconfiguration functionality (determinationand propagation of new parameter values).Body Graph: modeling construct in PSDF for core signal processing functionality associated with an application.Slide13

Case Study of Channelizer/receiver Application (3)

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Platform for Channelizer: Raspberry Pi 3 Model BDevice for Power Measurement: Tektronix Keithley Series 2280 Precision Measurement DC Power Supply Average processing power of all the available configurations.Slide14

Case Study of Channelizer/receiver Application (4)

14Simulation results for MDP-I.

Comparison among MDP-generated policies and fixed-configuration designs.In addition to providing better energy efficiency compared to the fixed configuration designs, our MDP approach can be configured systematically to generate a much larger set of trade-off options (Pareto-optimized fronts)

ensures optimality (with respect to the given reward function)Slide15

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Solver running timeMDP Solver: MATLAB-based open source solver, named MDPSOLVEPlatform for MDP Solver: Processor: i7-4710HQ running at 2.50GHzRAM: 12.0 GBOS: 64-bit Windows 10MDP-I solver running time: 294msMDP-II-a solver running time: 50.8msMDP-II-b solver running time: 41.5msIn a deployment with a fixed processing system (MDP-II-b) and changing external environment (MDP-II-a), the hierarchical MDP scheme reduces the solver time from 294ms to 50.8ms, which is a factor of over 5.7X smaller.

Model sizeMDP-I: 1.63MBMDP-II: 265kBMDP-II reduces model size by a factor of over 6.1X.Slide16

Conclusions

16We propose the HMCSM framework for design and implementation of adaptive embedded signal processing systems.HMCSM stands for Hierarchical MDP framework for Compact System-level Modeling.HMCSM provides a structured methodology that integrates dataflow

methods; MDP formulation using compact, hierarchical models; optimal policy generation at design time; and dynamic, system-level reconfiguration at run time.We demonstrate the effectiveness of our new MDP-based system design framework through experiments with an adaptive wireless communications receiver.Slide17

Thanks!

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