If you're a regular reader of this column, you know that I'm enthusiastic about the potential of "embedded vision" – the widespread, practical use of computer vision in embedded systems, mobile devices, PCs, and the cloud. Processors and sensors with sufficient performance for sophisticated computer vision are now available at price, size, and power consumption levels appropriate for many markets, including cost-sensitive consumer products and energy-sipping portable devices. This is ushering
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By now, most people who work with processors—whether in data centers, PCs, mobile devices, or embedded systems—understand that parallel processing is the way to get both high compute performance and good energy efficiency for most applications. And most of these people also realize that programming parallel processors is challenging. There are many different types of parallel processors, including CPUs with single-instruction/multiple data capabilities, multi-core CPUs, DSPs, GPUs and FPGAs,
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It's a very interesting time in embedded processors.
For decades, embedded processors have continued to deliver more performance and more features, at ever-lower prices and power consumption levels. Today, embedded systems designers are leveraging these processors to create an incredibly diverse range of innovative products. Some of these products, like the Nest thermostat and the G-Box MX2 set-top box, target high-volume markets.
Given all the new embedded systems being designed (and the large
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Smartphone application processor chips incorporate numerous processor cores, typically including multiple CPU cores, GPUs, DSPs, video processors, and image signal processors. Considering all the processing power available in these chips, why does Motorola's recently introduced Moto X smartphone use a stand-alone DSP processor chip? And why would Motorola use a DSP based on a 10-year-old architecture?
The answer is that the Moto X uses this low-power DSP chip to provide "always-on" voice
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Lately I've been spending many hours on conference calls: Early morning calls with colleagues in Europe and India, mid-day calls with customers in the U.S., and late evening calls with partners in China. I often find these calls difficult and fatiguing--not because of what people are saying, but because I frequently have trouble understanding what people are saying.
At first, I chalked this up to what seemed like the obvious explanations: "It's late in the day… It's my seventh call today… The
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I've been hearing a lot about "wearable tech" for the past year or so, and lately the buzz has intensified. At Qualcomm’s recent developer conference, the chipmaker unveiled its "Toq smartwatch," which was immediately followed with another smartwatch announcement by Samsung. And at the excellent Augmented World Expo conference in June, smart eyewear like Google Glass was a very hot topic, with wearable computer pioneer Steve Mann giving a riveting presentation and displaying his collection of
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Smartphones have become the most important application for high-performance, energy-efficient processors (see "ARM's 2015 Mid-Range Platform Prep: A 32-Bit Next-Step" in this month's edition of InsideDSP). That's because smartphones are a huge and growing business, and processors make a big difference in how smartphones perform – and how long their batteries last. As a result, interest has been growing in smartphone processor performance, and there's been quite a bit of benchmarking activity.
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Recently I heard a presentation from a start-up chip supplier promoting a new type of programmable architecture for baseband processing in cellular base stations and handsets. The company's CEO contended that digital signal processors (DSPs) are becoming passé, soon to be replaced by more modern architectures. This caused me to think about the future of DSPs.
Industry pundits have been heralding the death of DSPs for over a decade. And there's some evidence to support their view. For example
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In my December column, I observed that smartphones and tablets are starting to be used in places where purpose-built embedded systems once reigned, such as point-of sale terminals. At home, for example, I have a small Android tablet that I use as an Internet audio player. And my local sandwich shop uses iPads as self-service ordering and payment terminals.
When I first began thinking about this phenomenon approximately a year ago, I thought it was an interesting trend that might someday become
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Soon after BDTI got its start in the early 1990s, we became known for our benchmarks. We benchmarked whatever types of processors people were using for embedded digital signal processing: first DSPs, then CPUs, and eventually MCUs, FPGAs, and GPUs, too.
One of the interesting things about benchmarking processors for embedded digital signal processing tasks is the importance of optimization. Optimization is central to digital signal processing applications. In a typical embedded DSP application
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