Search Results for "4x"

... accelerators. Quantized inference is significantly faster than floating point inference. For example, models that we’ve run on the Qualcomm® Hexagon™ DSP rather than on the Qualcomm® Kryo™ CPU have resulted in a 5x to 15x speedup. Plus, an 8-bit model also has a 4x smaller memory footprint relative to a 32-bit model. However, often when quantizing a machine learning model (e.g., from 32-bit floating point to an 8-bit fixed point value), the model accuracy is sacrificed.

The typical use case for this high speed Node.js module is to convert large images in common formats to smaller, web-friendly JPEG, PNG, AVIF and WebP images of varying dimensions. Resizing an image is typically 4x-5x faster than using the quickest ImageMagick and GraphicsMagick settings due to its use of libvips. Colour spaces, embedded ICC profiles and alpha transparency channels are all handled correctly. Lanczos resampling ensures quality is not sacrificed for speed. As well as image...

Share compute and cache with Earthly Satellites. Build remotely from the comfort of your terminal. Satellites use the code in your current directory, stream logs back to you in real time, and output results (images, artifacts) back to your laptop. When used from a CI, Earthly Satellites can be used to speed up builds, as the cache is retained between runs. Earthly was engineered for readability. Most engineers are able to read an Earthfile without any prior knowledge. We took some of the...

The objective of this project is to create a hybris accelerator for design and development of projects in an ecommerce suite.