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  Date: 12/12/2012

Vivante claims its IP cores are widely adopted in ADAS automotive applications

Vivante has reported its IPcores have been widely adopted to power the next generation ADAS Computer Vision applications from top tier automotive OEMs. ADAS safety technologies use sensor fusion to combine inputs from cameras, sensors, GPS, RADAR, and IR to create “intelligent” cars that are very aware of their surroundings. These real time systems continuously monitor, predict, and try to prevent accidents. Automobiles from Acura, BMW, Ford, GM, Mercedes, and Volvo have safety packages that include different forms of ADAS.
ADAS is predicted as fast adopting technology both due to market as well as safety regulations.

“We definitely see OpenCL gaining momentum as the industry emphasizes safety, features, and convenience,” said Dan Loop, Freescale Semiconductor i.MX Automotive Product Marketing. “One reason for this rapid adoption of new safety features is the traction OpenCL is getting from OEMs and developers. The programmability and speed-up of some critical computer vision algorithms on the GPGPU provide significant performance gains and power savings over DSPs and CPUs. Using the OpenCL solution available in the i.MX 6 platform, our customers optimize their algorithms for hybrid systems to take full advantage of the GPU as well as the CPU.”

Vivante GPGPU solutions are built around the just released OpenCL 1.2 Full Profile specification that enables refinement of the most important parallel algorithms.

Vivante claims for certain classes of problems, the GPU is far more computationally and power efficient than other SIMD vector processors with a similar number of functional units. The GPGPU paradigm shines on "embarrassingly data parallel" problems, which are ubiquitous in Computer Vision applications like ADAS. More specifically, computations requiring heavy use of floating-point arithmetic and/or image warping and resampling will see significant benefits, according to Vivante.

Vivante says as part of the heterogeneous ADAS architecture in the Freescale i.MX 6 automotive grade applications processor, Vivante cores process incoming data streams from cameras and other sensors to accelerate the parallel portions of CV algorithms. Typical ADAS computations that see significant speed-up on the GPU include SURF, SIFT, Hough Transform, Canny or Sobel edge detectors, HOGS (Histogram of Oriented Gradients), Integral Image, and point cloud processing. These algorithms can also be used in other areas ranging from Eye Tracking, Face and Gesture Recognition Natural User Interfaces, classical Computer Vision, and advanced image processing for CE, Industrial, and Mil-Aero products.

“OpenCL on the GPU has now become a fundamental part of the latest heterogeneous compute architectures. ADAS is one key area where the jump from GPU research exercise to real world implementation has been made. We are proud to partner with Freescale’s automotive team to bring leading safety and computer vision features to the next generation of cars,” said Wei-Jin Dai, President and CEO of Vivante.
Author: Srinivasa Reddy N
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