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SSP Re-hosting System Development: CLBM Overview and Module Recognition. SSP Team Department of ECE Stevens Institute of Technology Presented by Hongbing Cheng. Outline. Background Generic CLBM Rule in Signal Processing Domain

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ssp re hosting system development clbm overview and module recognition

SSP Re-hosting System Development: CLBM Overview and Module Recognition

SSP Team

Department of ECE

Stevens Institute of Technology

Presented by Hongbing Cheng

outline
Outline
  • Background
  • Generic CLBM Rule in Signal Processing Domain
  • CLBM for various implementation Codes in Signal Processing Domain (progress overview)
  • Module Recognition: Program Understanding
semantic signal processing based radio re hosting
Semantic Signal Processing based Radio Re-hosting
  • Objective:
    • Theoretical level: Information integration and knowledge sharing in signal processing domain
    • Practical level: Re-hosting of radio implementations among heterogeneous platforms to facilitate the reconfiguration in CR/SDR systems
  • Approach:
    • Abstraction, Representation and Inference (ARI): Information exchange through three steps: abstraction of primitives, semantics-based representation, inference and code generation;
    • Cognitive linguistic behavior modeling (CLBM): Establish a semantic modeling framework for signal processing domain based on cognitive linguistics to guide the semantic ARI.
semantic signal processing based radio re hosting1
Semantic Signal Processing based Radio Re-hosting

Parse cognitive-linguistics-based representation and generate implementation code in the target platform

Represent the implementation profile of signal processing modules/systems based on cognitive linguistics

Abstract conceptual primitives (“Thing, Place, Path, Action, Cause”) from existing implementations of signal processing modules/systems in source code

semantic signal processing based radio re hosting2
Semantic Signal Processing based Radio Re-hosting
  • Prototype Demo
    • Illustrate the workflow of the proposed ARI re-hosting
    • Show some use cases to validate the idea

Abstraction of primitives and Representation with XML

Inference and Code Generation

semantic signal processing based radio re hosting3
Semantic Signal Processing based Radio Re-hosting
  • Tasks in this period
    • Establish more complete and accurate CLBM rules in signal processing domain
    • Develop CLBM for more languages based on the rules
    • Develop ARI demo for various source and target languages based on the CLBM
    • Investigate module recognition algorithm to build more abstract CLBM for SP systems
generic clbm rule in signal processing domain
Generic CLBM Rule in Signal Processing Domain
  • Semantic Primitives in Cognitive linguistics
    • Thing: the fundamental neonatal gestalts
    • Place: interaction among things
    • Path: associate places in a sequence for a purpose
    • Action: Things move down paths
    • Cause: Thing that initiate or constraint action
  • CLBM: Fit the knowledge of signal processing implementation profiles into the above semantic framework
generic clbm rule in signal processing domain1
Generic CLBM Rule in Signal Processing Domain
  • Generic CLBM Rule for Signal Processing
    • A Signal is a “Thing”
    • A Signal processing system/block to be represented is a “Path”
    • The signal (“Thing”) moves along the signal processing system/block (“Path”) is an “Action”
    • Input/output ports and signal processing modules inside the “Path” is “Places”, where different signals have interactions; Attributes of a thing are also “Places”, which could be interacted with other things
    • A control signals that controls a signal processing flow is “Cause”
generic clbm for signal processing implementations
Generic CLBM for Signal Processing Implementations
  • Hierarchical and Dynamic Properties of CLBM
    • A “Thing” may have many “Places” to interact

e.g., The power and the size of a signal are two “places” of the “thing” signal

    • A “Thing” could also be contained in different “places” to take different “actions”

e.g., A signal could be inside a module’s input place or output place to take the action “input” or “output”

generic clbm rule in signal processing domain2
Generic CLBM Rule in Signal Processing Domain
  • Hierarchical and Dynamic Properties of CLBM
    • A “Path” contains multiple “Places”

e.g., A transmitter could be composed of a channel coder and a modulator

    • A “Place” at the upper level could be a “Path” at the lower level

e.g., A modulator is a ‘Place’ in a transmitter, while itself could be represented by a ‘Path’ composed of several places: LUT, Up-converter,…

clbm for implementation codes in signal processing domain
CLBM for Implementation Codes in Signal Processing Domain
  • CLBM for different coding languages are required in radio re-hosting
    • Heterogeneous hardware or software platform
  • Language Elements Considered in Modeling
    • Syntax
    • Data Structure
    • Control Structure
    • Core Library
clbm for implementation codes in signal processing domain1
CLBM for Implementation Codes in Signal Processing Domain
  • Current work and Progress

More languages

More statements/syntaxes

sp module recognition program u nderstanding
SP Module Recognition: Program Understanding

Multi-Level Abstraction

Semantic Representation

Radio Level Abstraction

Radio Primitive

Program Understanding

Computational Primitive

Computational Level Abstraction

Program Understanding

Code Primitive

Programming Code

Code Level Abstraction

Cognitive Linguistics

sp module recognition program understanding
SP Module Recognition: Program Understanding
  • Background of Program Understanding
    • Static Analysis: relies on source code and documentation
      • Graph parsing approach: GRASPR system, Linda M. Wills, MIT, Ph.D. Dissertation,1992. It translates the program into a language-independent, graphical representation.
      • Knowledge-based approach: The idea is to keep programs as plans in knowledge base, and compare the target program to these plans.
      • Program similarity evaluation techniques: Compare the implementation styles and structures of programs
    • Dynamic Analysis: focuses on a system’s execution (incompleteness, scalability)
      • Execution trace analysis
sp module recognition program understanding1
SP Module Recognition: Program Understanding
  • Preliminary Consideration About Module Recognition

More and more accurate, more and more complex

The previous step could reduce the search space of the next step

Recognition based on function name, comments (text understanding)

Recognition based on features (Knowledge-based program understanding)

Recognition based on tree matching (Program similarity evaluation)

Validation based on Simulation

Coarse classification

Some possible resultswith different belief probabilities

Accurate matching

Validation

sp module recognition program understanding2
SP Module Recognition: Program Understanding
  • Feature-based recognition (knowledge based)
    • Based on the correlation between the radio behavior pattern and some features
    • Features
      • Lower level primitives: The radio level primitives (radio modules) are composed of computational level and code level primitives or other radio primitives. Therefore, those primitives are natural features.
      • Control structure: sequential structure; selection structure; repeat structure
      • Input/output variable type and range: For example, modulators have binary input and real output; while demodulators have real input and binary output.
      • Simulation results: The most intelligent way is to test the code and get some simulation results. For example, we could get constellations to differentiate different modulation types.
sp module recognition program understanding3
SP Module Recognition: Program Understanding
  • Feature-based recognition (knowledge based)