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Image recognition systems are growing increasingly sophisticated – but they don’t come close to matching the efficiency of the ones we carry around with us inside our skulls. As part of an effort to close that gap, our Jetson TX1 embedded computing module swept both tracks of the recent Low Power Image Recognition Challenge, held in Austin, Texas, at the IEEE Rebooting Computing event.
We’ve invested substantial resources in the power efficiency of Jetson’s GPU compute architecture. In gaming and professional design, this means fluid framerates on a frugal power budget. But in the realm of computer vision, performance per watt enables rapid control loops and near real-time responsiveness from an autonomous machine, such as a drone or a robot.
The Low Power Image Recognition Challenge began when NVIDIA’s David Kirk and Yung-Hsiang Lu at Purdue University, decided that image recognition on a power budget was a worthy challenge. The first two years presented modest challenges, with smaller groups of researchers, Yung-Hsiang notes. He plans to expand the competition over the coming years, including larger prizes.
We’ve invested substantial resources in the power efficiency of Jetson’s GPU compute architecture. In gaming and professional design, this means fluid framerates on a frugal power budget. But in the realm of computer vision, performance per watt enables rapid control loops and near real-time responsiveness from an autonomous machine, such as a drone or a robot.
The Low Power Image Recognition Challenge began when NVIDIA’s David Kirk and Yung-Hsiang Lu at Purdue University, decided that image recognition on a power budget was a worthy challenge. The first two years presented modest challenges, with smaller groups of researchers, Yung-Hsiang notes. He plans to expand the competition over the coming years, including larger prizes.
