- AMD's ROCm: CUDA Gets Some Competition
- CEVA-X1 DSP Core Targets Cellular IoT Opportunities
- Jeff Bier’s Impulse Response—Will Computer Vision Upend the Automotive Industry?
- Case Study: Balancing the Demands of Algorithms and the Capabilities of Processors When Designing Computer Vision Systems
- Wave Computing Targets Deep Learning
Earlier this week, Google announced the spin-off of its self-driving car project into a stand-alone business. Will Google become a major player in the automotive industry? Today, that idea seems far-fetched. On the other hand, 15 years ago Apple was a personal computer company, and few would have
For humans, it goes without saying that vision is extremely valuable. When you stop to think about it, it’s remarkable what a diverse set of capabilities is enabled by human vision – from reading facial expressions, to navigating complex three-dimensional spaces (whether by foot,
Look back over the history of processors, and you'll see many examples of tasks initially restricted to running on high-end processors that, once they became popular and standardized, eventually attracted specialized co-processor or processor support ( Figure 1 ). Consider, for example, video
Based on the pace of investment and acquisitions, and the level of buzz (some would say "hype") surrounding augmented reality and virtual reality, it is obvious that these technologies are hot. With good reason, I think. Augmented and virtual reality have long held enormous
If you’ve read recent editions of this column, you know that I believe that embedded vision – enabling devices to understand the world visually – will be a game-changer for many industries. For humans, vision enables many diverse capabilities: reading your spouse’s
The Internet of Things is an interesting phenomenon. Most people involved in technology seem to believe that the IoT growing fast, and that it represents a big opportunity. But most people also find it difficult to say exactly what the Internet of Things actually is , and how,
Last summer , I wrote that the time was ripe for deployment of neural networks in mass-market applications. Last week, Google validated this point of view by announcing that is has developed a specialized processor for neural networks (dubbed the "Tensor Processing Unit," or TPU),
April 13, 2016 | Write the first comment.
Convolutional neural networks (CNNs) and other "deep learning" techniques are finding increasing use in a variety of detection and recognition tasks: identifying music clips and speech phrases, for example, and finding human faces and other objects in images and videos. As a result,
We've been hearing a lot about autonomous cars lately – and for good reasons. Driverless cars offer enormous opportunities for improved safety, convenience, and efficiency. Their proliferation may have as profound an impact on our society as conventional automobiles have had over the past
It's no secret that sensors are proliferating. Our smartphones, for example, contain accelerometers, magnetometers, ambient light sensors, microphones – over a dozen distinct types of sensors. A modern automobile contains roughly 200 sensors. As sensors proliferate, the amount of data