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

1. SSP Re-hosting System Development: CLBM Overview and Module Recognition SSP Team Department of ECE Stevens Institute of Technology Presented by Hongbing Cheng

2. Outline • Background • Generic CLBM Rule in Signal Processing Domain • CLBM for various implementation Codes in Signal Processing Domain (progress overview) • Module Recognition: Program Understanding

3. 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.

4. 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

5. 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

6. 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

7. 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

8. 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”

9. Generic CLBM Rule for Signal Processing Implementations

10. 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”

11. 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,…

12. 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

13. CLBM for Implementation Codes in Signal Processing Domain • Current work and Progress More languages More statements/syntaxes

14. 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

15. 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

16. 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

17. 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.

18. SP Module Recognition: Program Understanding • Feature-based recognition (knowledge based)

19. Thank You