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Deep learning set to revolutionise computer, automotive vision

01 Apr 2015  | Junko Yoshida

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Some claim machines are gaining the ability to recognise objects as accurately as humans. According to a paper recently published by a team of Microsoft researchers in Beijing, their computer vision system based on deep CNNs had for the first time eclipsed the ability of people to classify objects defined in the ImageNet 1000 challenge. Only five days after Microsoft announced it had beat the human benchmark of 5.1 per cent errors with a 4.94 per cent error grabbing neural network, Google announced it had one-upped Microsoft by 0.04 per cent.

Different players in the electronics industry are tackling deep learning in different ways, however.

Different approaches

Nvidia CEO at GTC

Nvidia CEO at GTC

Nvidia, for example, is going after deep learning via three products. CEO Jen-Hsun Huang trotted out during his keynote speech at GTC Titan X, Nvidia's GeForce gaming GPU that the company describes as "uniquely suited for deep learning." He presented Nvidia's Digits Deep Learning GPU training system, a software application designed to accelerate the development of high-quality deep neural networks by data scientists and researchers. He also unveiled Digits DevBox, a deskside deep learning appliance, specifically built for the task, powered by four TITAN X GPUs and loaded with DIGITS training system software.

Asked about Nvidia's plans for its GPU in embedded vision SoCs for advanced driver assistance system (ADAS), Danny Shapiro, senior director of automotive, said Nvidia isn't pushing GPU as a chip company. "We are offering car OEMs a complete system, both cloud and a vehicle computer that can take advantage of neural networks."

A case in point is Nvidia's DRIVE PX platform, based on the Tegra X1 processor, unveiled at the International Consumer Electronics Show earlier this year. The company describes Drive PX as a vehicle computer capable of using machine learning, saying that it will help cars not just sense but "interpret" the world around them.

Deep learning on cars

How deep learning helps a car 'interpret' objects on the road (Source: Nvidia)

Conventional ADAS technology today can detect some objects, do basic classification, alert the driver, and in some cases, stop the vehicle. Drive PX goes to the "next level," Nvidia likes to say. Shapiro noted that Drive PX now has the ability to differentiate "an ambulance from a delivery truck."

By leveraging deep learning, a car equipped with Drive PX, for example, can "get smarter and smarter, every hour and every mile it drives," claimed Shapiro. Learning that takes place on the road feeds back into the data centre and the car adds knowledge via periodic software updates, Shapiro said.

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