Mobile phones are rapidly and dramatically expanding beyond their historical usage as voice-only communications devices, adding a variety of wireless data-fed text, email, web browsing and other functions, supplementing (and in many cases supplanting) the facilities of dedicated still and video cameras, and serving as portable multimedia playback platforms. But all of these functions consume power, and both users and designers of mobile phones are very concerned about battery life. Similarly,
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The October issue of IEEE Spectrum Magazine includes an interesting article titled "Could Supercomputing Turn to Signal Processors (Again)?" which discusses the viability of developing supercomputers using digital signal processors. It covers, among other things, a recent analysis project co-staffed by engineers at Texas Instruments and researchers at the University of Texas, Austin, to compare the floating-point operation-per-watt capabilities of TI's DSPs against those of other and now-more-
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Touch-free gesture interfaces are increasingly entering the public consciousness, spurred on by trendsetting popular implementations such as Microsoft's Kinect. And, as Tom Cruise's portrayal of Chief John Anderton in the future-forecasting film Minority Report suggests, they're equally compelling beyond the game console. Camera (i.e. image sensor)-based gesture interface implementations may be most common nowadays, but they're not the only feasible approach.
Several weeks ago, for example,
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In my October column, I explored the phenomenon of mobile application processors (the brains of smartphones and tablets) competing against more traditional types of embedded processors for use in embedded systems. But after writing that column, something happened that made me realize that mobile application processors don't necessarily have to be designed into embedded systems in order to compete against other kinds of embedded processors. Smartphones and tablets themselves are already
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Semiconductor memory is increasing in capacity and cost-effectiveness all the time. Yet, there are plenty of deeply embedded applications for which every spare byte of RAM or flash memory is a precious commodity, especially those leveraging memory integrated onto an SOC of processor, rather than external discrete memory. Throw in a performance-constrained processor (intentionally speed-limited to minimize power consumption), a small battery, and a multi-day battery life requirement, and you've
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In last month's edition of InsideDSP, Jeff Bier's editorial discussed the advantages of (and potential issues with) designing an embedded system around an application processor originally developed for smartphones, tablets, and other high volume devices. To wit, at the beginning of his writeup, Bier mentioned that Texas Instruments had recently stated its intentions to de-emphasize application processors for smartphones and tablets, instead refocusing its OMAP ARM-based SoCs on embedded
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In reading the InsideDSP newsletter every month, one observation that I hope you've made is that a diversity of processing options exist for implementing digital signal processing algorithms. The alternatives include GPUs, FPGAs, conventional CPUs, and standalone DSPs, along with DSP cores embedded alongside CPUs and other function blocks in highly integrated SoCs. And in recent years, you can also add microcontrollers (MCUs) to the list; while they don't typically include a standalone embedded
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We tend to think of digital signal processing as being computationally demanding, since it typically involves applying math-intensive algorithms to real-time streaming data. And, compared with many other types of embedded processing tasks, digital signal processing does indeed make heavy demands on processors. But that doesn't mean that DSP always requires expensive hardware.
Lately, I've been impressed by the growing array of microcontrollers with digital signal processing features. Often
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The prodigious transistor budgets delivered by modern semiconductor processes enable designers to create powerful processor cores and chips. However, this silicon potential will be for naught if it can't easily be harnessed by algorithm developers. Consider the non-trivial die area and development time consumed by a processor core, along with the notable competitive differentiation that can be accrued by its effective utilization. Clearly, the ease by which coders can gain robust access to
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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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