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Multi-objective refinement of Mapping Tables in Telecom Network Applications

This paper presents a multi-objective refinement approach for mapping tables in telecom network applications. The study explores different mapping table implementations, including primitives and multi-layer structures, and proposes an automated method for fast and efficient exploration and optimization. The results demonstrate the impact of network and operation characteristics on the performance, area, and power consumption of the mapping tables.

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Multi-objective refinement of Mapping Tables in Telecom Network Applications

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  1. Multi-objective refinement of Mapping Tables in Telecom Network Applications Jürgen Lambrecht, Chantal Ykman-Couvreur, Armin van der Togt, Francky Catthoor The Protocol Processor Project (PRO3)Contract No IST-1999-11449

  2. Outline • Introduction • Context • Example: network switch • Related Work • Our contribution • Design space and Method • Mapping table implementations: primitives • Primitives characteristics • Mapping table implementations: multi-layer • Exploration Method • System Level Cost Factors • Results • Multi Objective Exploration • Impact of network • Impact of operation characteristics • Future work and Conclusions

  3. Context Design Technology for Integrated Information and Communication Systems Memory system: bottleneck Methodology: system level specification  optimised embedded implementation Data type refinement Mapping tables Application domain: telecom network, database, multimedia, ...

  4. ATM MUX ATM 2 1 Y port 1 MUX 2 5 x Port 2 2 5 Y 4 7 x Key1 ( VPI[8] ) Key3 ( port[8] ) { VPI, VCI, port } [32] Key2 ( VCI[16] ) 2 = 0000 0010 5 = 0000 0000 1 = 0000 0001 4 = 0000 0100 0000 0101 7 = 0000 0000 0000 0111 2 = 0000 0010 2 = 0000 0010 1 = 0000 0000 1 = 0000 0001 2 = 0000 0010 0000 0001 5 = 0000 0000 0000 0101 2 = 0000 0010 Example: network switch Table (with active connections) 16 284 MB

  5. Related Work • Programming theory • STL • SETL • Embedded systems • imec - Wuytack et al. 1996 • Design space characterisation for mapping tables • Preliminary exploration and optimisation method • imec - Ykman et al. ISSS 1999 • Extended design space (key merging/splitting) • Improved cost function

  6. Power Area Our contribution • Objective trade-off • With all relevant application characteristics • Automated fast exploration and optimisation • simulates the application • collects profiling info • runs the tool • pareto curves result • Goal: • optimised implementation • trade-off multiple objectives • taking all relevant system characteristics into account

  7. Outline • Introduction • Context • Example: network switch • Related Work • Our contribution • Design space and Method • Mapping table implementations: primitives • Primitives characteristics • Mapping table implementations: multi-layer • Exploration Method • System Level Cost Factors • Results • Multi Objective Exploration • Impact of network • Impact of operation characteristics • Future work and Conclusions

  8. data data data data key key key key data data data data Mapping table implementations: primitives Array (AR) data data data data Pointer Array (PA) Linked List (LL)

  9. Primitives' characteristics

  10. LL(7) Layer 1 key1 key1 key1 key1 PA(4) 2 4 Layer 2 AR(3) Layer 3 data data data data data data data Mapping table implementations: multi-layer

  11. ATM 2 1 Y port 1 MUX 2 5 x Port 2 4 7 x 2 5 Y Key1 ( VPI[8] ) Key3 ( port[8] ) { VPI, VCI, port } [32] Key2 ( VCI[16] ) 2 = 0000 0010 5 = 0000 0000 1 = 0000 0001 4 = 0000 0100 0000 0101 7 = 0000 0000 0000 0111 2 = 0000 0010 2 = 0000 0010 1 = 0000 0000 1 = 0000 0001 2 = 0000 0010 0000 0001 5 = 0000 0000 0000 0101 2 = 0000 0010 Exploration method • Key ordering • Key hashing • Key concatenation • Key splitting

  12. LL(7) key1 key1 key1 key1 AR(3) data data data data data data data Exploration method • Key ordering • Key hashing • Key concatenation • Key splitting • Assignment of primitives Layer 1 PA(4) 2 4 Layer 2 Layer 3 Cost vector calculation

  13. PA(4) 2 4 data data data data data data data System Level Cost Factors • Application characteristics • Data: size, average number • Keys: size, average number, dependencies, frequency distributions • Operations: accesses and proportion AR(3) • Memory cost • Area: bits • Performance: accesses • Power: vendor model

  14. Outline • Introduction • Context • Example: network switch • Related Work • Our contribution • Design space and Method • Mapping table implementations: primitives • Primitives characteristics • Mapping table implementations: multi-layer • Exploration Method • System Level Cost Factors • Results • Multi Objective Exploration • Impact of network • Impact of operation characteristics • Future work and Conclusions

  15. Difference Factor 2 in size 5 in power 8 in accesses 2 in performance Multi Objective Exploration

  16. Impact of network

  17. Impact of operation characteristics

  18. Future work • Better memory library • Off-chip memory support • On/Off-chip partitioning • Include fixed platforms (w. caches…) • Further automation • New primitive data structures • Internet multimedia data sets • Trees

  19. Conclusions • Bottleneck in embedded system synthesis: • memory performance, area, and power • our focus: Mapping Tables • Optimisation at system level • Automated method needed • Multi-objective exploration • All relevant application characteristics • Fast • Power-objective result • 90% area savings • 80% power savings • without decreasing the performance

  20. www.imec.be Worldwide collaboration with more than 450 companies and institutes. IMEC – Kapeldreef 75 – B-3001 Leuven – Belgium – Tel. +32 16 281211 – Fax +32 16 229400 – www.imec.be

  21. ADT Selected data structures VirtualMemorySegments Physical Memories Memory Management in Matisse Abstract Data TypeRefinement Dynamic Memory Mngnt. Virtual memory mngntRefinement Physical memory mngntRefinement Physical Memory Mngnt.

  22. Exploration and Optimization Power 20,000 10,000 2,000 1,000 200 100 Layers 2 1 3 4

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