XPERT OPINION
PHASE 3: HARDWARE CONVERSIONS AND FUTURE EVOLUTIONS The final portion of the project employed Xilinx’s actual videoanalytics IP blocks (in place of the earlier simulation models), running on the Spartan ®-3A 3400 Video Starter Kit. A MicroBlaze™ soft processor core embedded within the Spartan-3A FPGA, augmented by additional dedicatedfunction blocks, implemented the network protocol stack. That stack handled the high-bit-rate and Ethernet-packetized metadata transfer to the BDTI-developed secondary processing algorithms, now comprehending both road sign detection and pedestrian detection and tracking. And whereas these algorithms previously executed on an x86-based PC, BDTI successfully ported them to an ARM® Cortex™-A8derived hardware platform called the BeagleBoard (Figure 4). Those of you already familiar with Xilinx’s product plans might right now be thinking of the Zynq™ Extensible Processing Platform, which combines the FPGA and Cortex-A8 CPU on a single piece of silicon. Might it be possible to run the entire video-analytics reference design on a single Zynq device? The likely answer is yes, since the Zynq product family includes devices containing sufficient programmable logic resources, and since the BDTI algorithms put only a moderate load on the ARM CPU core. Embedded vision is poised to become the next notable technology success story for both systems developers and their semiconductor and software suppliers. As the case study described in this article suggests, FPGAs and FPGA-plus-CPU SoCs can be compelling silicon platforms for implementing embedded vision processing algorithms. First Quarter 2012
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