The electronics industry has long been a dynamic one, but never as dynamic as it is today. Private equity investors now own some of the largest semiconductor companies and are pushing for improved efficiencies. Many, if not most, of the largest chip companies are making significant adjustments in their strategies. And, as usual, changes in technology—often driven by innovative start-ups—threaten to disrupt the status quo.

Digital video is almost everywhere. And where it isn’t now, it soon will be.  As a result, the market for digital video intellectual property components—hardware, software, you name it—is wide open, with lots of opportunities for money-making.  And there are roughly five buzillion vendors jockeying for position within a highly fragmented field.

VeriSilicon has released a licenseable DSP core, the ZSP800, and an associated development platform, VZ.AudioHD, optimized for “HD” audio applications.

Earlier this month, Catalytic announced that it had acquired Celoxica’s electronic system level (ESL) business. Catalytic is a start-up company that sells a MATLAB-to-C translation tool used to accelerate simulation and implementation of signal processing algorithms. Celoxica, on the other hand, developed a C-to-FPGA translation tool for creating hardware implementations of computationally demanding algorithms.  Put the two tools together, and what do you get?

The breathtaking advances in digital integrated circuits over the past 40 years—with current chips topping one billion transistors—have been possible in large part because designers and users have been able to ignore a number of “second-order” aspects of circuit behavior.  Unfortunately, as process geometries continue to shrink, some of those second-order effects have been promoted to first-order headaches.

To obtain the most efficient code, DSP software must be optimized at four distinct levels. First, the software architecture and data flow must be designed to take maximum advantage of the processor’s resources. Second, the appropriate data types must be selected—too big and you’re wasting resources, too small and your system may not work. Third, the software must be optimized at the algorithm level—perhaps by combining multiple algorithms into a single processing step, or by substituting one algorithm for another.

For a while there, it seemed as though DSP processors and general-purpose processors (GPPs) were morphing into one another. In an effort to provide better DSP performance, general-purpose processors (GPPs) were incorporating increasingly powerful DSP-oriented features. Meanwhile, as digital signal processing applications got more complex, DSP processors were becoming more CPU-like to enable efficient compilers and support more elaborate operating systems. It was getting hard to tell the DSPs and GPPs apart.