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TULIPP H2020 Project presentation @ FPGA Network: Implementing Machine Vision with FPGA and SoC Platforms

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TULIPP (Towards Ubiquitous Low-Power Image Processing Platforms) presentation @ NMI Event, in MBDA, Stevenage, UK– May 18 2017 By Flemming Christensen, Sundance, UK

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TULIPP H2020 Project presentation @ FPGA Network: Implementing Machine Vision with FPGA and SoC Platforms

  1. 1. This project has received funding from the European Union’s Horizon 20 20 research and innovation programme under grant agreement No 688403 TULIPP Place : Date : By: Flemming CHRISTENSEN, Sundance, UK Towards Ubiquitous Low-Power Image Processing Platforms
  2. 2. 8x Partners – 20+ Engineers – 3 years • Thales : Coordinator & Medical use case • Sundance : Hardware • Hipperos : Operating system • Synective Labs : ADAS use case • Efficient Innovation : Management • Fraunhofer IOSB : UAV use case • Ruhr Universität Bochum : FPGA tools • NTNU : Performance tools
  3. 3. How will we do it? WP7: Management, Coordination LABEL : Marketing, Ecosystem and Pre-normalisation WP6: IP protection, Dissemination, Communication, Advisory Board and Exploitation preparation WP1: Reference platform definition (Interfaces & implementation Rules) Instantiations WP2: Hardware WP4: Programming Toolchain WP3: Runtime, API, Libraries & OS feedback WP5 : Usecases description and Integration and platform validation
  4. 4. Pick a SoC, please
  5. 5. Typical Processing Platform Component tools Operating System Processor Toolchain Reference Platform Memory IO Processor
  6. 6. TULIPP pciked Xilinx SoCs • SoC + FPGA SoM • MPSoC + FPGA SoM 40mm x 50 mm – Zynq Z7030 40mm x 50 mm – Zynq ZU5EV
  7. 7. TULIPP System Node
  8. 8. Low-Power Image Processing RTOS Needs OS - high reliability, - low power, - hard real-time - high performance This kind of RTOS is not (yet) available today, as current GPOS (e.g. Linux) or RTOS lack one or more of the required features and performance. Specific Image processing Needs - supporting the hardware accelerators - the libraries needed for image processing.
  9. 9. STHEM: The TULIPP Tool-chain Status: • Xilinx SDSoC has been extended to support the current platform • Support for HIPPEROS OS is underway Insights: • Significant effort has been invested into the development of vendor tools • STHEM fills the productivity gaps between existing tools Support uTilities for Heterogeneous Embedded image processing (STHEM) •Supports development for all platform components •Map source files of the application to the appropriate tool chain •Retrieve OS configuration from the developer Development and Mapping •Boot OS with selected configuration (if needed due to changed configuration) •Update files (binaries, bitfiles, etc.) •Initialise the reconfigurable logic (if needed) •Start the application with the requested instrumentation Runner •Analyses performance results and presents findings to the developer Analyser
  10. 10. Medical imaging use case TDLP RAW IMAGE THALES Processing Unit CI / ICS UI GigE-Vision + Msg THALES Flat panel detector Customer system UI GigE-Vision + Msg CI / ICS Nano Processing Unit Inside the detector Based on SoC’ based Small-Form-Factor board Customer system THALES Flat panel detector Before TULIPP After TULIPP
  11. 11. Medical imaging use case • Real-Time X-Ray imaging for surgery • Reduce radiation dose by 75% • Add noise removal processing with critical real-time constraints
  12. 12. Pedestrian detection Safety application Car integration The Use Case ADAS use case
  13. 13. Unmanned Aerial Vehicle (UAV) use case • Performs real-time stereo depth estimation to do obstacle / collision avoidance (for an UAV), i.e. to detect obstacles in direction of flight • Based on dual cameras
  14. 14. Advisory Board and EcoSystem Advisory Board (WP6) Reference Platform (WP1) TULIPP Guide, implementation and demos Ask for review / advise Roles in the project: Provide information about standards Give feedback on the approach Early adopters SystemEco
  15. 15. EcoSystem Members
  16. 16. Holy-grail of TULIPP in Year 2020
  17. 17. Handbook Overview
  18. 18. Guidelines
  19. 19. Guidelines Advice: Exploit both vectorization and multithreading for high performance on multicore processors with vector units such as the ARM Cortex A9. On these architectures, utilizing all hardware execution resources is key to achieve high performance [2] [4, 5]. Recommended implementation method: Use OpenMP. OpenMP is a widely supported parallel programming API that enables programmers to express vectorization and multithreading operations concisely using compiler directives. Programmers need not worry about specifying scheduling and synchronization operations in code. These are handled transparently by the OpenMP runtime system. See the official OpenMP examples[6] to understand in more detail about exploiting vectorization and multithreading simultaneously.
  20. 20. “Thanks + Questions = Bye”