Areaefficient And Lowpower Implementation Of Vision Chips Using Multilevel Mixedmode Processing

• doi: 10.1109/CNNA.2014.6888638
• title: Area-efficient and low-power implementation of vision chips using multi-level mixed-mode processing
• publisher: IEEE
• isbn: 978-1-4799-6007-1
• issn: 2165-0144
• rank: 1676
• access_type: LOCKED
• content_type: Conferences
• abstract: Miniaturized low-power implementation of a vision system is critical in battery-operated systems such as wireless sensor network (WSN), micro-air-vehicles (MAV), and mobile phones. Conventional digital-intensive processing uses the raw image with huge redundancy which degrades the power and speed. This paper reports multi-level mixed-mode processing schemes for efficient VLSI implementation in terms of power, area and speed. In this approach, the processing is distributed in pixel-level, column-level and chip-level processors. Each processor operates in mixed-mode, analog and digital, domains for an optimal use of resources. Three vision chips have been designed and characterized to show the effectiveness of this approach. First, motion detection and feature extraction are implemented in an object-adaptive CMOS image sensor to remove temporal and spatial redundancies for low power operation. Second, a neuromorphic algorithm is implemented for optic flow generation in mixed-mode circuits. Event-driven analog processing units allow low power operation of pre-processing, while the digital processor provides the robustness of backend processing. Finally, background light subtraction is implemented in a 3-D camera for outdoors mobile applications. The reconfigurable pixel array implemented by pixel-merging and super-resolution could achieve faster processing and better background light suppression.
• article_number: 6888638
• pdf_url: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6888638
• html_url: https://ieeexplore.ieee.org/document/6888638/
• abstract_url: https://ieeexplore.ieee.org/document/6888638/
• publication_title: 2014 14th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA)
• conference_location: Notre Dame, IN, USA
• conference_dates: 29-31 July 2014
• publication_number: 6879364
• is_number: 6888588
• publication_year: 2014
• publication_date: 29-31 July 2014
• start_page: 1
• end_page: 2
• citing_paper_count: 0
• citing_patent_count: 0
• download_count: 397
• insert_date: 20140901
• index_terms:
  • ieee_terms:
    • Program processors
    • Optical sensors
    • Redundancy
    • Motion detection
    • Feature extraction
    • Spatial resolution
  • dynamic_index_terms:
    • Multi-level Process
    • Low-power Implementation
    • Vision Chips
    • Processing Unit
    • Raw Images
    • Image Sensor
    • Camera Sensor
    • Motion Detection
    • Motion Sensors
    • Optical Flow
    • Depth Camera
    • RGB-D Camera
    • Depth Sensor
    • 3D Camera
    • Wireless Sensor Networks
    • Background Light
    • Multilevel Strategy
    • Multi-level Scheme
    • Optimal Use Of Resources
    • Low-power Operation
    • Power Consumption
    • Redundant Data
• isbn_formats:
  • format: Electronic ISBN, value: 978-1-4799-6007-1, isbnType: New-2005
• authors:
  • Author Name: Jihyun Cho
    Affiliation: Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
    Author URL: https://ieeexplore.ieee.org/author/38238016500
    ID: 38238016500
    Order: 1
    Author Affiliations:
    • Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
  • Author Name: Seokjun Park
    Affiliation: Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
    Author URL: https://ieeexplore.ieee.org/author/38240901800
    ID: 38240901800
    Order: 2
    Author Affiliations:
    • Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
  • Author Name: Jaehyuk Choi
    Affiliation: Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
    Author URL: https://ieeexplore.ieee.org/author/37066872300
    ID: 37066872300
    Order: 3
    Author Affiliations:
    • Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
  • Author Name: Euisik Yoon
    Affiliation: Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
    Author URL: https://ieeexplore.ieee.org/author/37275464600
    ID: 37275464600
    Order: 4
    Author Affiliations:
    • Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA

Image Sensor

• sensor_type: CMOS
• resolution: Not specified
• dynamic_range: Not specified
• pixel_size: Not specified
• dark_current: Not specified

Optical Data

• focal_length: Not specified
• aperture: Not specified
• field_of_view: Not specified
• distortion: Not specified

Performance Metrics

• power_consumption: 3.4 µW

Applications & Benefits

• cell_imaging: Surveillance applications and outdoor mobile applications.
• benefits: Low power operation, ability to reduce data redundancy, feature extraction for motion detection.

Supporting Organizations

• supported_by: University of Michigan

Manuscript Details

• publication_date: 29-31 July 2014

Relevancy Score

• score: 8
• missing_fields:
  • resolution
  • dynamic_range
  • pixel_size
  • dark_current
  • focal_length
  • aperture
  • field_of_view
  • distortion
  • frame_rate
  • signal_to_noise_ratio
  • sensitivity
  • shutter_speed
  • noise

Processed JSON Filename

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