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Embedded vision: Growing opportunities for FPGAs

Posted: 25 Jul 2012     Print Version  Bookmark and Share

Keywords:embedded vision  robotics  surveillance  FPGA 

What upcoming innovation can aid in the design of a system that warns users to a child struggling in a swimming pool, or to an intruder attempting to break into a residence or business? It�s the same technology that can alert drivers of impending hazards on the roadway, and even prevent them from executing lane-change, acceleration and other maneuvers that would be hazardous to themselves and others. It can equip a military drone or other robot with electronic �eyes� that enable limited-to-full autonomous operation. It can assist a human physician in diagnosing a patient�s illness. It can uniquely identify a face, subsequently initiating a variety of actions (automatically logging into a user account, for example, or pulling up relevant news and other information), interpreting gestures and even discerning a person�s emotional state. And in conjunction with GPS, compass, accelerometer, gyroscope and other features, it can deliver a data-augmented presentation of a scene.

The technology common to all of these application examples is embedded vision, which is poised to enable the next generation of electronic-system success stories. Embedded vision got its start in traditional computer vision applications such as assembly line inspection, optical character recognition, robotics, surveillance and military systems. In recent years, however, the decreasing costs and increasing capabilities of key technology building blocks have broadened and accelerated vision's penetration into key high-volume markets.

Driven by expanding and evolving application demands, for example, image sensors are making notable improvements in key attributes such as resolution, low-light performance, frame rate, size, power consumption and cost. Similarly, embedded vision applications require processors with high performance, low prices, low power consumption and flexible programmability, all ideal attributes that are increasingly becoming a reality in numerous product implementation forms. Similar benefits are being accrued by latest-generation optics systems, lighting modules, volatile and nonvolatile memories, and I/O standards. And algorithms are up to the challenge, leveraging these hardware improvements to deliver more robust and reliable analysis results.

Embedded vision refers to machines that understand their environment through visual means. By "embedded," we're referring to any image-sensor-inclusive system that isn't a general-purpose computer. Embedded might mean, for example, a cellular phone or tablet computer, a surveillance system, an earth-bound or flight-capable robot, a vehicle containing a 360° suite of cameras or a medical diagnostic device. Or it could be a wired or wireless user interface peripheral; Microsoft's Kinect for the Xbox 360 game console, perhaps the best-known example of this latter category, sold 8 million units in its first two months on the market.

FPGA opportunity
A diversity of robust embedded vision processing product options exist: microprocessors and embedded controllers, application-tailored SoCs, DSPs, graphics processors, ASICs and FPGAs. An FPGA is an intriguing silicon platform for realising embedded vision, because it approximates the combination of the hardware attributes of an ASIC—high performance and low power consumption—with the flexibility and time-to-market advantages of the software algorithm alternative running on a CPU, GPU or DSP. Flexibility is a particularly important factor at this nascent stage in embedded vision's market development, where both rapid bug fixes and feature set improvements are the norm rather than the exception, as is the desire to support a diversity of algorithm options. An FPGA's hardware configurability also enables straightforward design adaptation to image sensors supporting various serial and parallel (and analogue and digital) interfaces.

The Embedded Vision Alliance is a unified worldwide alliance of technology developers and providers chartered with transforming embedded vision's potential into reality in a rich, rapid and efficient manner (see sidebar). Two of its founding members, BDTI (Berkeley Design Technology, Inc.) and Xilinx, partnered to co-develop a reference design that exemplifies not only embedded vision's compelling promise but also the role that FPGAs might play in actualizing it. The goal of the project was to explore the typical architectural decisions a system designer would make when creating highly complex intelligent vision platforms containing elements requiring intensive hardware processing and complex software and algorithmic control.

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