Page 18

EETE FEB 2016

ENSW & CETHNOLOGY neural networks Google’s deep learning comes to Movidius By Junko Yoshida Movidius, an ultra-low-power computer vision-processor startup best known for its partnership with Google on Project Tango, has extended its relationship with Google. This time, the collaboration is focused on neural network technology, with plans to accelerate the adoption of deep learning in mobile devices. In an interview with EE Times, Remi El-Ouazzane, CEO, Movidius, called the agreement “a new chapter” in the partnership. In Project Tango, Google used a Movidius chip in a platform that uses computer vision for positioning and motion tracking. The project’s mission was to allow app developers to create user experience that works on indoor navigation, 3D mapping, physical space measurement, augmented reality and recognition of known environments. The new agreement with Google is all about machine learning. It’s intended to bring super intelligent models—extracted from deep learning at Google’s data centers – over to mobile and wearable devices. El-Ouazzane said Google will purchase Movidius computer vision SoCs and license the entire Movidius software development environment, including tools and libraries. Google will deploy its advanced neural computation engine on a Movidius computer vision platform. Movidius’ vision processor will then “detect, identify, classify and recognize objects, and generate highly accurate data, even when objects are in occlusion,” El-Ouazzane explained. “All of this is done locally without Internet connection,” he added. The public endorsement from Google will boost the startup, said Jeff Bier, a founder of the Embedded Vision Alliance. The announcement is also “interesting,” he added, because it shows “Google has a serious interest in the use of deep learning for mobile and embedded devices.” It demonstrates that Google’s commercial interest in artificial neural networks isn’t limited to their use in data centers. Different teams within Google, including its machine intelligence group (Seattle), are involved in this agreement with Movidius. Google will be developing commercial applications for deep learning. Movidius is “likely to get more input from Google, and get opportunities – over time – to optimize its SoC for Google’s evolving software,” Bier speculated. Movidius’ agreement with Google is unique. “Not everyone has access to Google’s well-trained neural networks,” said El- Ouazzane, let alone the opportunity to collaborate on computer vision with the world’s most prominent developer of machine intelligence. Asked if the work with Google involves the development of embedded vision chips for autonomous cars (i.e. Google Cars), Movidius CEO El-Ouazzane said, “Google intends to launch a series of new products based on the technology. I can’t speak on their behalf. But the underlying technology – high quality, ultra-low power for embedded vision computing – is very similar” whether applied to cars or mobile devices. For now, however, Movidius’ priority is getting its chip into mobile and wearable devices. El-Ouazzane said, “Our embedded vision SoCs are to the IoT space, as Mobileye’ chips are to the automotive market.” Mobileye today has the lion’s share of the vision chip market for Advanced Driver’s Assistance Systems. Fig. 1: Movidius’ embedded vision SoC. The search giant is hungry for technology to “recognize human speech, objects, images, emotion on people’s faces, or even fraud detection,” explained Bier. “Google has a commercial interest in understanding context better, when people are searching for certain things.” However, both physical objects and human emotions are ambiguous, and they come with infinite variability, Bier said. They pose hard problems for computers that tend to seek solutions “in formulaic ways.” “These are the tasks we don’t know how to write instructions for. They have to be learned by examples,” as Blaise Agϋera y Arcas, head of Google’s machine intelligence group in Seattle, said in Movidius’ promotional video. In recent years, though, “machine learning is cracking the problem,” said Bier. Before the emergence of Convolutional Neural Networks (CNN) in computer vision, algorithm designers had to design many decisions through many 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. In other words, as Bier summed up: “Traditional computer vision took a very procedural approach in detecting objects.” With deep learning, however, designers “don’t have to tell computers where to look. Deep learning will make decisions for them.” With all the learning and training carried out in the artificial neural network, “Computers are now gaining intuition,” said Bier. Obviously, Google wants to expand this technology beyond the data center, by bringing it to mobile devices used in the real world. Keeping decisions local In theory, Google’s Android device, equipped with an embedded vision-processing chip, would know more about its user and predict better the user’s needs. It could intuit and provide what the user wants, even when the device is offline. Picture, for example, a swarm of mobile, wearable devices or even drones equipped with Movidius’ embedded vision SoC, said El-Ouazzane. They can autonomously classify and recognize objects in an unsupervised manner. And they don’t have to go back to the cloud. When connected, the devices can send less detailed metadata back to the trained network. The network in return sends back updated layers and weights learned 18 Electronic Engineering Times Europe February 2016 www.electronics-eetimes.com


EETE FEB 2016
To see the actual publication please follow the link above