Video

Jeff Bier’s Impulse Response—One Sensor to Rule Them All

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 generated by these sensors grows too, of course. But different types of sensors produce vastly different amounts of data. As Chris Rowen, CTO of Cadence's IP group, recently pointed out in an excellent Read more...

Case Study: From Platform Selection to Optimized Application: Vision System Design Success

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 its clients, BDTI's skill in benchmarking has armed it with unique skill in optimizing software to best exploit processor capabilities. It's also provided BDTI with an in-depth understanding of the Read more...

Imagination Technologies' New GPUs Tackle Computer Vision Arithmetic

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 company is more fully embracing computer vision requirements with its two new PowerVR Series7XT Plus GPU cores. And, reading between the lines of a recent briefing, Imagination Technologies continues to seriously Read more...

NVIDIA Focuses on Automotive at CES

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 information presented highlighted the growing importance of automotive applications not only to NVIDIA and its semiconductor competitors, but also to their shared customers as well as to their customers, i.e. Read more...

Jeff Bier’s Impulse Response—Unlocking the Value in Video

Today, billions of hours of video are collected each year, but most of it is never used, because we don't have a practical way to extract actionable information from it. A new generation of computer vision solutions, powered by deep neural networks, will soon change this, unleashing the tremendous value that's currently locked away in our video files. As a kid in the late 1970s, I remember some of the early consumer video cameras. They were very big, very heavy, and very expensive. In the Read more...

Next-Gen Cadence Tensilica Vision Processor Core Claims Big Performance, Energy Consumption Gains

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 performance in many applications and therefore further expanding the core's vision processing function reach. And in October of this year, Cadence further extended the product line, unveiling its latest Read more...

Freescale Buys CogniVue: The BDTI Interview

In September, Freescale announced its acquisition of computer vision processor IP supplier CogniVue. BDTI discussed the news with Matt Johnson, Vice President and General Manager of Freescale's Automotive MCU division, which instigated the transaction. The two companies had closely collaborated for the past several years, so the purchase wasn't a complete surprise. Still, the interview produced a number of interesting insights. Also in attendance was Simon Morris, former CEO of CogniVue (now a Read more...

Case Study: BDTI's Expert, Independent Analysis Enables Optimum Vision Processor Selection

A growing number of products are incorporating computer vision capabilities. This, in turn, has led to rapid growth in the number of processors being offered for vision applications.  Selecting the best processor (whether a chip for use in a system design, or an IP core for use in an SoC) is challenging, for several reasons. First, these processors use very diverse architecture approaches, which makes it tough to compare them. Second, because vision applications and algorithms are also quite Read more...

CEVA Software Framework Brings Deep Learning to Embedded Vision Systems

As Jeff Bier has mentioned in several of his recent columns, deep learning algorithms have gained prominence in computer vision and other fields where there's a need to extract insights from ambiguous data. Convolutional neural networks (CNNs) – massively parallel algorithms made up of layers of computation nodes – have shown particularly impressive results on challenging problems that thwart traditional feature-based techniques; when attempting to identify non-uniform objects, for example, or Read more...

Qualcomm's QDSP6 v6: Imaging and Vision Enhancements Via Vector Extensions

Qualcomm has been evolving its in-house DSP core for many years. Originally developed for use in Qualcomm’s cellular modems, more recently it has also found use as an application co-processor, offloading multimedia tasks from the CPU in smart phones and tablets. Earlier InsideDSP articles covered the v4 "Hexagon" DSP core in mid-2012 and early 2013, along with the v5 Hexagon architecture later that same year. Now, with its Hexagon v6 DSP core, which will see its first silicon implementation in Read more...