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
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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
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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
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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.
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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
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As embedded processors and applications become increasingly complex, good benchmarks are more important than ever. System designers need good benchmarks to judge whether a processor will meet the needs of their applications, and to make accurate comparisons among processors. Processor developers need good benchmarks to assess how their processors stack up against the competition, and to prove their processors' capabilities to customers.
But what exactly comprises a good benchmark?
One obvious
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Hear the words "high volume DSP market" and you might automatically think of "mobile phones". And you'd be right; recent estimates peg quarterly worldwide mobile phone shipments approaching half a trillion units, with smartphones (which often contain multiple DSP cores) representing three-quarters of that amount. However, CEVA believes that in the not-too-distant future, alternative markets with similar cellular connectivity needs—wearables, connected vehicles, and a diversity of IoT devices—
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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
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Since reading Malcolm Gladwell's Blink a decade ago, I've been intrigued by how the mind works – particularly how judgements and decisions are made. I've been inspired to take an armchair tour of research on this topic, and have encountered fascinating insights from the likes of David Eagleman and Daniel Kahneman.
Reading the work of these talented researchers and writers has led me to the inescapable conclusion that most of our judgements and decision-making take place in our subconscious
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Algorithms are the essence of digital signal processing; they are the mathematical "recipes" that transform signals in useful ways. Companies developing new algorithms, or considering purchasing or licensing algorithms, often need to assess whether an algorithm will fit within their processing budget—and thereby within their cost and power consumption targets.
But estimating an algorithm's processing load can be difficult if the algorithm has not already been carefully mapped onto the target
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