Software Development

Computer vision promises to be the key to the next set of "killer apps"—computer vision-enabled apps that will leverage artificial intelligence to help keep us safer and healthier. But computer vision algorithms are compute- and power-intensive, and need to process large amounts of
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HPC (high-performance computing) servers, which have notably embraced the GPGPU (general-purpose computing on graphics processing units) concept in recent years, are increasingly being employed for computer vision and other deep learning-based applications. Beginning in late 2014, NVIDIA
NVIDIA was an early and aggressive advocate of leveraging graphics processors for other massively parallel processing tasks (often referred to as general-purpose computing on graphics processing units, or GPGPU). The company's CUDA software toolset for GPU computing has to date secured only
Hard on the heels of the public release of CEVA's second-generation convolutional neural network toolset, CDNN2 , the company is putting the final touches on its fifth-generation processor core, the CEVA-XM6 , designed to run software generated by that toolset. Liran Bar, the company's
Last year, when CEVA introduced the initial iteration of its CDNN (CEVA Deep Neural Network) toolset, company officials expressed an aspiration for CDNN to eventually support multiple popular deep learning frameworks. At the time, however, CDNN launched with support only for the well-known Caffe
Modern SoCs increasingly contain a variety of processing resources: one or more CPU cores and a GPU, often with a DSP, programmable logic, or one or multiple special-purpose co-processors for tasks such as computer vision. Properly harnessed, such heterogeneous processors often deliver impressive
In late January of this year, Movidius and Google broadened their collaboration plans, which had begun with 2014's Project Tango prototype depth-sensing smartphone . As initially announced , the companies’ broader intention to "accelerate the adoption of deep learning within mobile
Convolutional neural networks (CNNs) and other "deep learning" techniques are finding increasing use in a variety of detection and recognition tasks:  identifying music clips and speech phrases, for example, and finding human faces and other objects in images and videos. As a result,
As computer vision is deployed into a variety of new applications, driven by the emergence of powerful, low-cost, and energy-efficient processors, companies need to find ways to squeeze demanding vision processing algorithms into size-, weight-, power, and cost-constrained systems. Fortunately for
Algorithms are the essence of digital signal processing; they are the mathematical "recipes" that transform signals in useful ways. Companies developing new algorithms, or considering purchasing or licensing algorithms, often need to assess whether an algorithm will fit within their