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 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.
How deep learning helps a car 'interpret' objects on the road
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 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.
Qualcomm teased about its cognitive-capable platform, which will be a part of the new Snapdragon application processor for mobile devices, but said very little about its building blocks. The company explained that the Zeroth platform is capable of “computer vision, on-device deep learning and smart cameras that can recognize scenes, objects, and read text and handwriting.”
Qualcomm pitches its first cognitive computing platform
Meanwhile, Cognivue (Quebec, Canada) sees the emergence of CNN creating a level playing field for embedded-vision SoCs.
Cognivue is a designer of its own Image Cognition Processor core, tools and software, used by companies such as Freescale. By leveraging Cognivue’s programmable technology, Freescale provides intelligent imaging and video solutions for automotive vision systems.
Tom Wilson, vice present of product management at Cognivue, said, “We are finding our massively parallel image processing architecture and datapath management ideally suited for deep learning.” In contrast, competing approaches have often hand designed their embedded vision SoCs to keep pace with the different vision algorithms that have emerged over time, which they’ve applied to their SoC design and optimized each time. They might find themselves stuck with old architecture ill-suited to CNN, he explained.
Cognivue's new Image Cognition Processing technology, called Opus, will leverage APEX architecture (shown above), and enable parallel processing of sophisticated Deep Learning (CNN) classifiers.
Robert Laganière, professor at the School of Electrical Engineering and Computer Science at University of Ottawa, told EE Times, “Before the emergence of CNN in computer vision, algorithm designers had to make many design decisions” involving a number of layers and steps with vision algorithms.
Such decisions include the type of classifier used for object detection and methods to build an aggregation of features (by using a rigid detector e.g. histogram). More decisions include how to deal with deformable parts of an object and whether to use cascade method (a sequence of small decisions to determine an object) or a support vector machine.
“One small specific design decision you make at each step of the way could have a huge impact in object detection accuracy,” said Laganière.
Next page: SoCs optimized for neural networks?