Industry Expert Blogs

As Jeff Bier has mentioned in several of his recent columns, deep learning algorithms have gained prominence in computer vision and other fields where there's a need to extract insights from ambiguous data. Convolutional neural networks (CNNs) – massively parallel algorithms made up of layers of computation nodes – have shown particularly impressive results on challenging problems that thwart traditional feature-based techniques; when attempting to identify non-uniform objects, for example, or in sub-optimal viewing conditions. However, as with many emerging technologies, much of the R&D work on CNNs is being undertaken on resource-rich PC platforms. CEVA's just-introduced Deep Neural Network (CDNN) software framework aspires to optimize CNN code and data for more modestly equipped embedded systems, specifically those based on the company's latest XM4 vision processor core.

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