- 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
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 Only seven months later , the Vision P5 has been superseded by the Vision P6 ( Figure 1 ).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
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
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
CEVA's DSP cores have long been a fixture in many mobile phone cellular modems, where they handle the tranceiver's digital signal processing functions, working alongside function-specific hardware accelerators. However, the physical layer coordination between the transmit and receiver
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
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
Just prior to the 2014 Consumer Electronics Show, Imagination Technologies unveiled its first computer vision processor offering with the announcement of the Raptor core architecture , the first product iteration of which was released at the February 2014 Mobile World Congress . Now, the
Automobile-based processing intelligence, both in the form of fully autonomous vehicles and more modest ADAS (Advanced Driver Assistance Systems), garnered exclusive billing in NVIDIA's keynote and booth at this year's Consumer Electronics Show, held last month in Las Vegas, Nevada. The
In 2013 , Tensilica (subsequently acquired by Cadence) released its second-generation image processing IP core, the IVP, which also supported modest computer vision capabilities ( Figure 1 ). One year later came the IVP-EP, which supported increased data precision flexibility, boosting overall