Anaconda Vision Processor

1
(No Transcript)
2
Anaconda Vision Processor

• Agenda
• Overview
• Typical applications
• Development on the Anaconda
• Q A

3
Anaconda Vision Processor

• Main Features
• Camera Link Acquisition plus image processing in
  1 PCI-X slot
• Versatile processing module to match customer
  performance/price target
• Flexible and easy to deploy

4
Anaconda Vision Processor-Data Flow
Data Sync
Acquisition
Image Formatter
Defect Correction
To Host
On-board Memory
Image Processing
Optional
5
Anaconda Vision Processor
Acquisition
Image Formatter
Defect Correction

• Acquisition Block
• Full Camera Link or Dual Base
• Support Area-scan and Line scan cameras(Shaft
  encoder, Frame trigger, etc)
• Include Trigger-to-Image reliability features

On-board Memory
Image Processing
6
Anaconda Vision Processor
Acquisition
Image Formatter
Defect Correction

• Image Formatter Block
• Support all current sensor architecture
• Multi-Taps, Multi-Channels
• Segmented, alternated
• Reverse Scanning direction (Horizontal and
  vertical)
• Tri-linear color sensors,etc

On-board Memory
Image Processing
7
Anaconda Vision Processor
Acquisition
Image Formatter
Defect Correction

• Defect Correction Block
• iLUT 4 Taps of 10 to 10-bits, 8 Taps of 8 to 8
  bits
• FLC(Line scan), FFC (area-scan)
• Gain x Pixel Value Offset for each pixel
  location in the image. Gain is between 1 and 2
• FPN Fixed-Pattern noise to correct pixel that
  cant be corrected by FFC-FLC (Gain larger than
  2)
• CMOS sensor with Dead or Hot pixels.

On-board Memory
Image Processing
8
Anaconda Vision Processor
Acquisition
Image Formatter
Defect Correction

• On-Board memory block
• Up to 2GB of on-board memory to store images
• Transfer from host or Acquisition supported
  (allow to test processing section from host
  images)
• Large bandwidth to memory allow multiple transfer
  to occurs simultaneously 5.3 GB/sec
• Transfers from/to memory are handle by highly
  optimized DMA engine

On-board Memory
Image Processing
9
Anaconda Vision Processor
Acquisition
Image Formatter
Defect Correction

• Image Processing Block
• Modular and Scalable architecture
• FPGA section for mathematic acceleration
• CISC processor for complex algorithm not suitable
  for FPGA implementation
• User can use existing design or design his own
  solution
• Custom processing module possible

On-board Memory
Image Processing
10
Anaconda Vision Processor

• Typical Application 1 Medical imaging
• Characteristics
• 1024 x 1024, 12-bits, 30 fps
• Noisy image
• Cost-sensitive
• Required processing
• Image correction
• Image Enhancement
• Display control

11
Anaconda Vision Processor

• Typical Application 1 Medical imaging(Contd)
• Image Correction
• Flat-field correction to guarantee field of view
  uniformity
• UnWarp compensate for camera mis-alignment and
  lens induced deformation
• Note UnWarp consist in a function that perform
  pixel relocation.

12
Anaconda Vision Processor

• Warp used to correct perspective
• Original image Corrected image

13
Anaconda Vision Processor

• Typical Application 1 Medical imaging(Contd)
• Image enhancement
• Frame averaging is a good way to eliminate
  gaussian noise
• When object move,blurring occurs. Motion
  detection is used to eliminate blurring
• Convolution 5x5 with programmable kernel for
  edge-enhancement

14
Anaconda Vision Processor

• Typical Application 1 Medical imaging(Contd)
• Display function Make doctor analysis easier.
• Rotation Any angle with 1/64 of a degree
  accuracy
• Horizontal Flip allow the doctor to align
  symmetrical images(left and right)
• Image Inversion (Negative)
• Anaconda can perform all theses steps in
  real-time with no Host CPU usage

15
Anaconda Vision Processor

• Typical Application 1 Medical imaging(Contd)
• Anaconda Demonstration

16
Anaconda Vision Processor

• Typical Application 2 Wafer Inspection
• Characteristics
• High-speed linescan camera
• Processing use Golden image subtraction
• Large reference images
• Amount of data to process 150-300 MB/sec
• Required processing
• Alignment
• interpolation (Affine transform)
• Subtraction, Threshold, RLE
• Large on-board memory

17
Anaconda Vision Processor

• Typical Application 2 Wafer Inspection(contd)
• Alignment process Execute Area-based search on
  CISC
• Minimize image transfer between host and Anaconda
• CISC has direct access to on-board memory
• Search results (Scaling, Rotation and
  Translation) are sent to processing FPGA

18
Anaconda Vision Processor

• Typical Application 2 Wafer Inspection(contd)
• Interpolation FPGA-based. Process data at 90
  MB/sec
• Search results are used to generate Affine
  transform matrix (valid for rigid body object)
• Interpolation (Affine transform) is accurate to
  sub-pixel and compensate for all three parameters

19
Anaconda Vision Processor

• Typical Application 2 Wafer Inspection(contd)
• Subtraction/Threshold/RLE FPGA perform at no
  cost.
• Operation is pipeline with interpolation step
• Results are available to CISC or sent down to the
  Host
• Data reduction make transfer time negligible
• Blob analysis is done on the host or CISC
• Solution 2-3 Anacondas can process in real-time
  the latest linescan camera (320MB/sec)

20
Anaconda Vision Processor

• Typical Application 3 Flat-Panel inspection
• Characteristics
• High-speed area scan camera
• Multiple processing branch in parallel
• Simple calculation but numerous
• Amount of data to process 300 MB/sec
• Required processing
• Operation performed simultaneously
• Multiple multiplications per pixel

21
Anaconda Vision Processor

• Typical Application 3 Flat-Panel inspection
• Solution
• FPGA processing has enough memory bandwidth
• Multiple multiplication performed in parallel
  (132 multiplication at 133 MHz)
• 17 556 Millions Multiplication per sec

Equivalent to 10 PCs running at 1.8 GHz
22
Anaconda Vision Processor

• Typical Application 4 Object Profiling
• Characteristics
• High-speed area scan camera
• Extremely high frame rate 10 000 fPS
• Need to horizontal find peak
• Amount of data to process 600 MB/sec
• Basler A504
• Required processing
• Detect maximums for each horizontal pixel
• Output only peak location with sub-pixel accuracy
• Typical applications PCB, FPD

23
Anaconda Vision Processor

• Typical Application 4 Object Profiling (contd)
• Solution
• On-board processing using FPGA
• Data reduction 30 times less data to host
• Host processing becomes easy
• High accuracy results with minimum Host CPU load
• Host CPU can handle multiple cameras
  simultaneously

24
Anaconda Vision Processor

• Other possible applications
• Print inspection
• AOI inspection
• Color image processing
• Document archiving with JPEG compression

25
Anaconda Vision Processor

• Algorithm Development on Anaconda
• Two options
• Done by Coreco Project description in SOW
• Customer develop himself Need Anaconda
  Development kit

26
Anaconda Vision Processor
IMAGE PROCESSING UNIT
DDR (512 MB)
2 GB/s
FPGA XC2VP20 or XC2VP30
Data to/From Main Board
1 GB/s
1.5 GB/s
SBSRAM (16 MB)
FPGA Register I/F
27
Anaconda Vision Processor

• Algorithm Development on Anaconda
• FPGA design can be done using
• Celoxica Handle-C High-level C like language for
  FPGA design
• VHDL using standard Xilinx development SW

28
Anaconda Vision Processor

• Algorithm Development on Anaconda
• FPGA section
• Coreco provide Celoxica or VHDL components to
  interface with the outside
• Image bus High-speed bus dedicated to image
  transfer
• FPGA internal registers
• Dedicated DDR memory banks
• High-speed SBSRAM memory banks

29
Anaconda Vision Processor

• Algorithm Development on Anaconda
• Complete Design Flow

30
Anaconda Vision Processor

• Algorithm Development on Anaconda
• Host application
• The host program needs to
• Configure the acquisition
• Load the FPGA binary file
• Configure Data transfers from/to the FPGA
• Write to FPGA internal registers

31
Anaconda Vision Processor

• Algorithm Development on Anaconda
• Host application use SaperaLT Classes
• New classes created for Anaconda
• Anaconda demo programs are using them
• Our Goal is???

Make host application development EASY
32
Anaconda Vision Processor

• Algorithm Development on Anaconda
• PowerPC development
• Need to use CodeWarrior cross-compiler from
  Metrowerks
• SW Project already exists to speed-up development
  process
• Anaconda PowerPC API make communication easy

33
Anaconda Vision Processor

• Algorithm Development on Anaconda
• Included in the PowerPC API
• Messaging between Host and PowerPC
• Altivec optimized Sapera processing library
• Advanced functions like search
• Note PowerPC has direct access to on-board DDR
• SW development is easy, no extra data transfer

34
Anaconda Vision Processor

• Anaconda Development Tools
• Part number exist for complete development system
• Include
• Anaconda
• PowerPC cross-compiler
• FPGA tools (Celoxica or Xilinx)
• Required Sapera Software
• Optional Tools FPGAChipscope or PowerPC
  PowerTap emulator

35
Anaconda Vision Processor

• Anaconda Development Tools
• Build-in Diagnostics
• Image Bus monitoring tool
• Detailed timing module Log transfers completion
  and other events

36
Anaconda Vision Processor

37
Anaconda Vision Processor

• Anaconda Competition
• Matrox Odyssee Family
• Not scalable
• ASIC lack flexibility
• SBS Tsunami
• Not a dedicated imaging company
• Difficult to use
• No CISC possible

38
Anaconda Vision Processor

• Questions???

39
Anaconda Vision Processor

• Flexibility
• Processing Power
• Competitive Price
• Three good reasons to design with the Anaconda
• Thanks