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The PC Camera
     A New Class of Smart Camera
(…Or How to put 90 Gflops of Processing to Good Use)




   VISION 2011, Stuttgart, November 10
Let’s Start with ‘Why’

XIMEA thinks you should be free to
demand cutting-edge performance,
industrial robustness and true
hardware/software compatibility from
your next compact vision system
without paying a premium.
Where the Machine Vision
    Market Is Today

          Maturity
              =
      Empowerment
              =
      Inflection Point
So What’s Next In the Evolution of
   Machine Vision Systems?
First, Ask Yourself:
• How optimal is traditional integration of
  components?
• Don’t we have huge overhead on
  protocols/stacks/Links/MACs/PHYs?
• Plethora of interfaces, components, sparse soft-
  and hardware-compatibility matrices


             ???WHY???
This               …..         Not This




The PC Camera
A fully-functional, high-performance
industrial PC inside the camera
PC Camera
Key components     Integration tools   PC Camera
Aspects of PC Camera
• Fully optimized data path from sensor to the application
   – Zero CPU overhead on image data delivery
   – True zero copy paradigm
   – Lowest possible latency
• Potential for integrated PLC to achieve sub-microsecond
  jitter
• Complexity of hard- and software interfaces handled by
  PC Camera vendor
Paradigm Shift
Paradigm Shift
PC Cameras Based on x86
• Sony, Matrox, NI, Leutron, Tattile, XIMEA all offer PC
  Cameras
• Wealth of existing frameworks and applications (usually
  tied to vendor’s full image processing library)
• Well-known operating systems (Linux, Windows,
  Full/Embedded)
• Well-known application development tools (C++, etc.)
• New algorithms are first developed on PC, not limited to
  sub-set of algorithms chosen by smart camera vendor
Atom PC Cameras –
          Pinnacle of Perfection?
• Raw CPU performance in the range of 3GFlops
• What if you want to connect more than one camera?
   – Runtime license cost
   – High-speed interfaces are limited
• Upgradeability of RAM and SSD
Computing Platforms

                                                             We are here
Single-thread Performance




                                                                           Enabled by:
                                                                           • Rich data Parallelism
                                                                           • Power-efficient GPUs
                                              Constrained by:
                                              • Power                      Constrained by:
                                              • Parallel SW availability   • Programming Models
                                              • Scalability
                            Constrained by:
                            • Power
                            • Complexity

                            Single-core era   Multi-core era               Heterogeneous
                                                                           computing era
New Era:
      Heterogeneous computing
• APU – Accelerated Processing Units
• Collocating of CPU and GPU on single die
   – CPU is used for OS and other infrastructure tasks
   – GPU is used for number crunching
• Disadvantage of shared memory become an advantage
  providing zero copy framework
• GPU is fully programmable with OpenCL and Direct-
  Compute
AMD Fusion family of APUs
AMD Fusion family of APUs
•   40nm process, Zacate
•   1x or 2x 64bit cores, 1.6GHz
•   9W and 18W TDP
•   2x32KB I and 2x32KB D L1 caches
•   2x512KB 16-way associative caches
•   MMX, SSE, SSE2, SSE3, SSE4a, AMD64
•   64 bit DDR3-1333 memory controller
•   80 shader cores running at 500MHz
•   4x PCIe Gen 2
•   APU zero copy path
•   OpenCL programmable
CURRERA-G
CURRERA-G:
Anatomy of an (Ultra-Compact) Giant
 •   AMD G processor, T56N or T40N
 •   A55E controller hub
 •   2GB DDR3 memory, up to 16GB SSD
 •   0.2 W/GFLOPS
 •   1x Gigabit Ethernet with PoE IEEE 802.3at Type 2
 •   1x HDMI output
 •   1x eSATA 3Gbps port
 •   3x USB 2.0 or USB3.0 (for multiple cameras)
CURRERA-G anatomy
CURRERA-G:
           What It Means to You
• PC Camera with high performance processor made for
  vector calculations and logic with true zero-copy memory
  access
• Full OS or Embedded OS
• OS Adds Software Flexibility While Improving Remote
  Support
• Lower latency than PC Host systems
• More than 25 API’s to the most popular image processing
  libraries on the market
• And one or two other benefits…
Heat Issues: ✓
• Dissipating >20W from compact enclosure is
  challenging and requires active cooling
• Micro heat-pipes
• Solid state microblowers
• Use of external connections
Embedded PLC vs. Latency: ✓
• Runs fully autonomous and independent
  from main CPU and its OS
• Less than 1µs jitter provides higher
  determinism than any RTOS can deliver
• Senses opto-isolated part or position
  detector inputs
• Receives results of image processing
  algorithm
• Controls opto-isolated outputs and
  programmable LED light controller
• Graphical programming requires no
  previous experience
• Programmable watchdog functionality,
  can also reboot main CPU and its OS
Compatibility: ✓
What’s Next?
• Hardware AMD, 2012
   – New A-Series APUs: Trinity, 32nm, 2.2GHz-3.1GHz, 2 and 4
     cores
   – Includes Turbo CORE and AMD Power Gating
   – DDR3-2133, Radeon HD 7000
• Intel response?
• Software
   – OpenCL infiltrates image processing libraries
   – Development of task and data parallel computational algorithms
• Full integration of computational architecture and operating
  systems.
Thank you
• Questions please




              www.ximea.com

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Ximea - the pc camera, 90 gflps smart camera

  • 1. The PC Camera A New Class of Smart Camera (…Or How to put 90 Gflops of Processing to Good Use) VISION 2011, Stuttgart, November 10
  • 2. Let’s Start with ‘Why’ XIMEA thinks you should be free to demand cutting-edge performance, industrial robustness and true hardware/software compatibility from your next compact vision system without paying a premium.
  • 3. Where the Machine Vision Market Is Today Maturity = Empowerment = Inflection Point
  • 4. So What’s Next In the Evolution of Machine Vision Systems?
  • 5. First, Ask Yourself: • How optimal is traditional integration of components? • Don’t we have huge overhead on protocols/stacks/Links/MACs/PHYs? • Plethora of interfaces, components, sparse soft- and hardware-compatibility matrices ???WHY???
  • 6. This ….. Not This The PC Camera A fully-functional, high-performance industrial PC inside the camera
  • 7. PC Camera Key components Integration tools PC Camera
  • 8. Aspects of PC Camera • Fully optimized data path from sensor to the application – Zero CPU overhead on image data delivery – True zero copy paradigm – Lowest possible latency • Potential for integrated PLC to achieve sub-microsecond jitter • Complexity of hard- and software interfaces handled by PC Camera vendor
  • 11. PC Cameras Based on x86 • Sony, Matrox, NI, Leutron, Tattile, XIMEA all offer PC Cameras • Wealth of existing frameworks and applications (usually tied to vendor’s full image processing library) • Well-known operating systems (Linux, Windows, Full/Embedded) • Well-known application development tools (C++, etc.) • New algorithms are first developed on PC, not limited to sub-set of algorithms chosen by smart camera vendor
  • 12. Atom PC Cameras – Pinnacle of Perfection? • Raw CPU performance in the range of 3GFlops • What if you want to connect more than one camera? – Runtime license cost – High-speed interfaces are limited • Upgradeability of RAM and SSD
  • 13. Computing Platforms We are here Single-thread Performance Enabled by: • Rich data Parallelism • Power-efficient GPUs Constrained by: • Power Constrained by: • Parallel SW availability • Programming Models • Scalability Constrained by: • Power • Complexity Single-core era Multi-core era Heterogeneous computing era
  • 14. New Era: Heterogeneous computing • APU – Accelerated Processing Units • Collocating of CPU and GPU on single die – CPU is used for OS and other infrastructure tasks – GPU is used for number crunching • Disadvantage of shared memory become an advantage providing zero copy framework • GPU is fully programmable with OpenCL and Direct- Compute
  • 15. AMD Fusion family of APUs
  • 16. AMD Fusion family of APUs • 40nm process, Zacate • 1x or 2x 64bit cores, 1.6GHz • 9W and 18W TDP • 2x32KB I and 2x32KB D L1 caches • 2x512KB 16-way associative caches • MMX, SSE, SSE2, SSE3, SSE4a, AMD64 • 64 bit DDR3-1333 memory controller • 80 shader cores running at 500MHz • 4x PCIe Gen 2 • APU zero copy path • OpenCL programmable
  • 18. CURRERA-G: Anatomy of an (Ultra-Compact) Giant • AMD G processor, T56N or T40N • A55E controller hub • 2GB DDR3 memory, up to 16GB SSD • 0.2 W/GFLOPS • 1x Gigabit Ethernet with PoE IEEE 802.3at Type 2 • 1x HDMI output • 1x eSATA 3Gbps port • 3x USB 2.0 or USB3.0 (for multiple cameras)
  • 20. CURRERA-G: What It Means to You • PC Camera with high performance processor made for vector calculations and logic with true zero-copy memory access • Full OS or Embedded OS • OS Adds Software Flexibility While Improving Remote Support • Lower latency than PC Host systems • More than 25 API’s to the most popular image processing libraries on the market • And one or two other benefits…
  • 21. Heat Issues: ✓ • Dissipating >20W from compact enclosure is challenging and requires active cooling • Micro heat-pipes • Solid state microblowers • Use of external connections
  • 22. Embedded PLC vs. Latency: ✓ • Runs fully autonomous and independent from main CPU and its OS • Less than 1µs jitter provides higher determinism than any RTOS can deliver • Senses opto-isolated part or position detector inputs • Receives results of image processing algorithm • Controls opto-isolated outputs and programmable LED light controller • Graphical programming requires no previous experience • Programmable watchdog functionality, can also reboot main CPU and its OS
  • 24. What’s Next? • Hardware AMD, 2012 – New A-Series APUs: Trinity, 32nm, 2.2GHz-3.1GHz, 2 and 4 cores – Includes Turbo CORE and AMD Power Gating – DDR3-2133, Radeon HD 7000 • Intel response? • Software – OpenCL infiltrates image processing libraries – Development of task and data parallel computational algorithms • Full integration of computational architecture and operating systems.
  • 25. Thank you • Questions please www.ximea.com