- Tensilica Vision P6 Processor Core Adopts Deep Learning-Focused Enhancements
- Movidius' Fathom Enables Embedded Deep Learning
- Jeff Bier’s Impulse Response—Will Neural Network Processors Become Mainstream?
- Case Study: Growing Your Business by Targeting New Markets
- Deep Learning and Digital Signal Processing: A Conversation with Cadence's Chris Rowen
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May 25, 2016 | Write the first comment.
In late January of this year, Movidius and Google broadened their collaboration plans, which had begun with 2014's Project Tango prototype depth-sensing smartphone . As initially announced , the companies’ broader intention to "accelerate the adoption of deep learning within mobile
One key challenge for a technology company is identifying markets for its products. Let’s say you have an idea for entering a new market using your existing technology. How do you know if there’s a fit? How do you realistically assess your risk? With more than 25 years of experience
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),
Just last October , Cadence announced the then-latest generation in its computer vision processor core roadmap, the Tensilica Vision P5. Only seven months later , the Vision P5 has been superseded by the Vision P6 ( Figure 1 ). This rapid product development pace reflects the equally rapid
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,
With more and more products incorporating computer vision functionality, semiconductor vendors are increasingly designing specialized processors for vision applications. For product developers, this means that identifying the best processor (whether a chip for use in a system design, or an IP core
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
ADAS (advanced driver assistance systems) are rapidly being incorporated into automobiles and other vehicles, as products unveiled at January's North American International Auto Show in Detroit, Michigan made clear. Just a few short years ago, passive collision warning and active collision
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
March 21, 2016 | Write the first comment.
As computer vision is deployed into a variety of new applications, driven by the emergence of powerful, low-cost, and energy-efficient processors, companies need to find ways to squeeze demanding vision processing algorithms into size-, weight-, power, and cost-constrained systems. Fortunately for