Imagination Technologies' new PowerVR Furian graphics microarchitecture is the company's most significant advancement since 2011's Rogue, which has formed the microarchitecture foundation of multiple subsequent product families (Figure 1).

GPS has proven to be an extraordinarily valuable and versatile technology. Originally developed for the military, today GPS is used in a vast and diverse range of applications. Millions of people use it daily for navigation and fitness. Farmers use it to manage their crops. Drones use it to automatically return to their starting location. Railroads use it to track train cars and other equipment.

Here at BDTI, we’re working to apply deep learning techniques to a wide range of applications. Deep learning can be extremely effective—if there’s the right combination of data and processing power. The challenge to success lies in understanding how much data is sufficient and how to process it efficiently. This is where BDTI’s expertise in algorithms and architectures delivers value to our customers.

At last month's MWC (Mobile World Congress), CEVA unveiled the XC12, its latest DSP core for communication infrastructure applications.

Jetson TX2 is NVIDIA's latest board-level product targeted at computer vision, deep learning, and other embedded AI tasks, particularly focused on "at the edge" inference (when a neural network analyzes new data it’s presented with, based on its previous training) (Figure 1).

It's remarkable to see the range of applications in which deep neural networks are proving effective – often, significantly more effective than previously known techniques. From speech recognition to ranking web search results to object recognition, each day brings a new product or published paper with a new challenge tamed by deep learning.

One of the most interesting resources for those interested in the rapidly growing field of deep learning is the "Cognitive Computing Startup List," compiled by Chris Rowan of Cognite Ventures. As of February 21, Chris had identified 275 companies that are, in his words, "the most focused, the most active and the most innovative…." These were culled from various lists of companies that tout artificial intelligence and deep learning.

Image-sensor maker Himax, processor core vendor CEVA, and algorithm provider emza Visual Sense have partnered to develop a low-power "always on" vision sensor module with integrated visual analytics. The three companies offered a conceptual demonstration of the product, dubbed the WiseEye IoT vision sensor, at last month's Consumer Electronics Show.

In recent months, evidence has continued to mount that artificial neural networks of the "deep learning" variety are significantly better than previous techniques at a diverse range of visual understanding tasks.

For example, Yannis Assael and colleagues from Oxford have demonstrated a deep learning algorithm for lip reading that is dramatically more accurate than trained human lip readers, and much more accurate than the best previously published algorithms.

Computer vision promises to be the key to the next set of "killer apps"—computer vision-enabled apps that will leverage artificial intelligence to help keep us safer and healthier. But computer vision algorithms are compute- and power-intensive, and need to process large amounts of data. These barriers have limited their use to enterprise, line-powered devices and cloud-assisted mobile devices. The implementation of computer vision algorithms on mobile processors, where compute resources are limited and power consumption must be curtailed, is key to wider use.