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Machine vision How will deep learning change SoCs? By Junko Yoshida Deep Learning is already changing the way computers see, hear and identify objects in the real world. However, the bigger -- and perhaps more pertinent -- issues for the semiconductor industry are: Will “deep learning” ever migrate into smartphones, wearable devices, or the tiny computer vision SoCs used in highly automated cars? Has anybody come up with SoC architecture optimized for neural networks? If so, what does it look like? “There is no question that deep learning is a game-changer,” said Jeff Bier, a founder of the Embedded Vision Alliance. In computer vision, for example, deep learning is very powerful. “The caveat is that it’s still an empirical field. People are trying different things,” he said. There’s ample evidence to support chip vendors’ growing enthusiasm for deep learning, and more specifically, convolutional neural networks (CNN). CNN are widely used models for image and video recognition. Earlier this month, Qualcomm introduced its “Zeroth platform,” a cognitive-capable platform that’s said to “mimic the brain.” It will be used for future mobile chips, including its forthcoming Snapdragon 820, according to Qualcomm. Cognivue is another company vocal about deep learning. The company claims that its new embedded vision SoC architecture, called Opus, will take advantage of deep learning advancements to increase detection rates dramatically. Cognivue is collaborating with the University of Ottawa. If presentations at Nvidia’s recent GPU Technology Conference (GTC) were any indication, you get the picture that Nvidia is banking on the all aspects of deep learning in which GPU holds the key. China’s Baidu, a giant in search technology, has been training Fig. 1: Search results of ‘cats that look like dogs’ (Source: Yahoo). deep neural network models to recognize general classes of objects at data centers. It plans to move such models into embedded systems. Zeroing in on this topic during a recent interview with EE Times, Ren Wu, a distinguished scientist at Baidu Research, said, “Consider the dramatic increase of smartphones’ processing power. Super intelligent models—extracted from the deep learning at data centers – can be running inside our handset.” A handset so equipped can run models in place without having to send and retrieve data from the cloud. Wu, however, added, “The biggest challenge is if we can do it at very low power. AI to Deep learning One thing is clear. Gone are the frustration and disillusion over artificial intelligence (AI) that marked the late 1980’s and early ‘90’s. In the new “big data” era, larger sets of massive data and powerful computing have combined to train neural networks to distinguish objects. Deep learning is now considered a new field moving toward AI. Some claim machines are gaining the ability to recognize 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 5 days after Microsoft announced it had beat the human benchmark of 5.1% errors with a 4.94% error grabbing neural network, Google announced it had one-upped Microsoft by 0.04%. Different players in the electronics industry are tackling deep learning in different ways, however. Different approaches 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 new GeForce gaming GPU which 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 Assitance 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. 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 Fig. 2: Nvidia CEO at GTC. into the data center and the car adds knowledge via periodic software updates, Shapiro said. Audi is the first company to announce plans to use the Drive PX in developing its future automotive self-piloting capabilities. Shapiro said Nvidia will be shipping Drive PX to its customers in May, this year. 14 Electronic Engineering Times Europe April 2015 www.electronics-eetimes.com


EETE APR 2015
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