QVGA

Computer Vision incorporates vision and machine-learning technology to allow for a machine to gain an understanding of its surroundings through visual recognition. Using optical sensory as an input, a machine equipped with machine-learning capabilities allows it to extract and classify its surroundings so that it can better “react” to the environment that it is in. This is part of the Artificial Intelligence (AI) initiative, where machines are trained to interpret sensory inputs so that they can behave and perform human-like tasks.

With an increasing number of AI applications operated on the EDGE, the ability to create low-power deep learning models becomes crucial in a battery-operated device. Similarly, visual sensors are no exception to these power constraints. To fuel the next wave of always-on sensing needs on these so-called edge devices, optical sensors need to also support the notion of always-on visual sensing.

PixArt’s Global Shutter Image Sensor product line are developed with unique power-saving architecture, which fully realizes the Ultra-Low-Power (ULP) Computer Vision technology of PixArt. With power consumption as low as 600uW at 30fps, the Global Shutter products are designed to provide visual means to AI machine-learning devices. 
 
Ultra-Low-Power Module
Part No. Package Dimension
(µm)
Lens FOV
Resolution
(Array Size)
Shutter Type
Max. Frame Rate
Avg. Operating Power Consumption
Interface
More Info
PAJ6100U6
4100*3900*2100
90 degrees
QVGA (320x240)
Global Shutter
30fps
1,400µW@30fps QGA; 500µW@30fps QQVGA
Pixel Data: Parallel 8-bit; Register Control: 4-wire SPI
Ultra-Low-Power Sensor
Part No. Package Type
Package Dimension
(µm)
Resolution
(Array Size)
Shutter Type
Max. Frame Rate
Avg. Operating Power Consumption
Interface
More Info
PAG7920LT
CSP
2240*2740*748
QVGA (320 x 240)
Global Shutter
180fps
0.79mW@15fps; 0.288mW@5fps; 0.072mW@1fps
Pixel Data: Parallel & SPI; Register Control: I2C
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