Processors

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The general trend of recent times is toward developing DSPs that are specialized for particular "killer" applications , such as wireless or embedded vision. Many other applications also need DSPs, however, and Cadence's Tensilica Fusion DSP core family, which complements the
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We're hearing more and more about the effectiveness of deep learning for a growing range of applications. With the surge in the volume of data available for training, and reduction in the cost of computing, technologists have turned to deep neural networks for solutions to compute-intensive
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
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In early 2015 , Synopsys' DesignWare EV5x processor core family achieved notable attention for its unique co-processor engine focused on CNNs (convolutional neural networks) for object recognition and other vision functions. The company's new EV6x processor core family includes an
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
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Just last October , Cadence announced the then-latest generation in its computer vision processor core roadmap, the Tensilica Vision P5. Only seven months later , the Vision P5 has been superseded by the Vision P6 ( Figure 1 ). This rapid product development pace reflects the equally rapid
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,
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With more and more products incorporating computer vision functionality, semiconductor vendors are increasingly designing specialized processors for vision applications. For product developers, this means that identifying the best processor (whether a chip for use in a system design, or an IP core
CEVA's DSP cores have long been a fixture in many mobile phone cellular modems, where they handle the tranceiver's digital signal processing functions, working alongside function-specific hardware accelerators. However, the physical layer coordination between the transmit and receiver