![]() ![]() This is done by presenting the developer with an abstract platform model that conceptualizes all of these architectures in a similar way, as well as an execution model supporting data and task parallelism across heterogeneous architectures. OpenCL allows developers to focus on applications rather than chip architectures via a single, portable source code base, providing a unified tool chain and language to target all of the parallel processors currently in use. Meanwhile OpenCL, the open and royalty-free programming standard for maximizing parallel compute utilization on heterogeneous systems, gives machine vision system designers a cross-platform, non-proprietary solution for accelerating their applications across mainstream processing platforms including APUs, SOCs and multicore CPUs and GPUs. OpenCV’s machine vision-optimized algorithms are commonly used today to detect and recognize faces, identify objects, classify actions in videos, track camera movements and moving objects, and even extract 3D models of objects. ![]() It provides real-time responsiveness and advanced intelligence for modern smart camera systems spanning applications including automated inspection and measurement, security and surveillance, and image detection and identification. ![]() The free for use, cross-platform operational OpenCV (Open Source Computer Vision) programming library has emerged as a key enabler for high-performance, parallel processing-driven computer vision applications. Open development tools like OpenCV and OpenCL are playing a major role in this effort. In order for machine vision system designers to most effectively take advantage of the increases in parallel processing performance provided by heterogeneous architectures, their programs must be written in a scalable fashion so as to run on the widest possible range of systems without coding modification. OpenCV/OCL for fast processing and code portability This introduces additional benefits for the applications hosted on these networks such as the ability to leverage the same applications for database management, security and remote management.Ĭollectively these efficiencies can help yield leaner cost structures for integrators and end users alike, and allows them an opportunity to overcome the hardware and software incompatibilities and cumbersome software maintenance processes that can result from different processor architectures deployed throughout a factory. The x86 architecture provides smooth interoperability with the growing IP-based factory infrastructure to help facilitate improved data management capabilities and provide tight integration with IT networks and x86-based distributed control systems. Offering PC-caliber performance and application agility complemented by a robust ecosystem of industry-standard, x86-optimized software, applications and development environments, x86 machine vision systems are unlocking myriad development, deployment and management efficiencies. And with a footprint of only 24.5mm x 24.5mm, the SOC simplifies design complexity, helping enable machine vision system designers to shorten design times and achieve aggressive form factor goals without sacrificing processing performance. With between 85 and 185 single precision GFLOPs of compute performance, such an architecture can help eliminate the need for FPGAs or DSPs to accelerate image processing. Recently introduced integrated system on chip platforms further reduce the APU’s two-chip architecture – the APU and the companion I/O controller hub – with the silicon-level integration of the I/O controller hub. With the APU, the silicon-level integration of a low-power x86 CPU and the parallel processing performance of a programmable, discrete-class general-purpose graphics processing unit (GPGPU) in a single device drives the high speed processing that’s essential for achieving high performance machine vision. The arrival of x86 accelerated processing units (APUs) enabled another major leap forward for machine vision technology. The relatively recent arrival of PC-based ‘smart cameras’ that forego conventional DSP and FPGA-based processing platforms heralds another significant advance in intelligent vision system technology as the industry shifts away from specialized legacy processors and narrowly-supported imaging software in favor of the more versatile x86 platform. Machine vision technology is evolving quickly, fueled by dramatic gains in processing performance through innovative heterogeneous architectures that leverage FPGAs, DSPs, and GPUs paired with a microprocessor, which accelerate image processing functions and handle data transfer and I/O respectively.
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