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c.WAVE120 is a fully hardwired deep learning inference super-resolution HW IP, supporting UHD resolution that upscales low-resolution data into high-resolution in real-time. c.WAVE120 performs this task by utilizing a massive set of training datasets.  When low-resolution images or videos are zoomed-in, the pixels appear broken and blurry, but that’s when our SR technique steps into action. c.WAVE120 extracts the feature points of an image or video, splits them pixel by pixel, applies the appropriate colors to fill in the missing parts of the data, stitches them, and then reproduces sharper, high-resolution images. c.WAVE120’s neural network was designed and trained to upscale video horizontally and vertically to two,  and four times larger with improved resolution results. For example, it does 4K UHD video to 8K UHD horizontally and vertically, and with a 1080p HD video, it can convert it to the 4K UHD format.

In modern technology, neural networks are used to implement neural processing units (NPU) to execute deep learning algorithms.

For the SR network, implementation is impracticable due to structural limitations, such as extremely high DRAM bandwidth requirements. This technology can be applied to various customer application product markets such as consumer electronics, automotive, home entertainment, IoT, surveillance cameras, and much more.

Product Specification: 

  • Performance: 8K (7680x4320) 60fps @550MHz​

  • Supported Input Image Format: YUV 400/420 (Optionally YUV 422)

  • The Supported Bit Width of In/Out Image: 8-/10-bit (Optionally 12-bit)

  • Operation Mode: M2M and OTF (On-the-Fly or DRAM-less) mode

    • Supporting frame compression and non-frame compression for the input/output pixel data of c.WAVE120​

  • Crop Mode: Supporting crop mode for the input image

  • Supported Scaling Ratio of Neural Network: The starting position and the size of crop region should be the multiple of 2

    • x2, x4​

  • Supported Scaling Ratio: An arbitrary scaling ratio between x1.0 and x8.0

    • The width of each input and output component (Y, Cb, and Cr) should be the multiple of 4​

  • Features Extraction​

  • Constructing HR Image

  • Cost-effective high-quality IP ​

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