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Source channel @githubtrending · Post #15271 · Nov 5

#cplusplus#arm#baidu#deep_learning#embedded#fpga#mali#mdl#mobile#mobile_deep_learning#neural_network Paddle Lite is a lightweight, high-performance deep learning inference framework designed to run AI models efficiently on mobile, embedded, and edge devices. It supports multiple platforms like Android, iOS, Linux, Windows, and macOS, and languages including C++, Java, and Python. You can easily convert models from other frameworks to PaddlePaddle format, optimize them for faster and smaller deployment, and run them with ready-made examples. This helps you deploy AI applications quickly on various devices with low memory use and fast speed, making it ideal for real-time, resource-limited environments. It also supports many hardware accelerators for better performance. https://github.com/PaddlePaddle/Paddle-Lite

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Libreware

@libreware · Post #957 · 06/22/2021, 01:28 PM

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— LibreCellular 21.04 documentation –https://librecellular.org/ The LibreCellular project aims to make it easier to create #4G cellular #networks with open source software and low cost software-defined radio (#SDR) hardware. Seeking to achieve this via validated hardware and software configurations that are subjected to rigorous testing, together with additional tooling and #documentation for repeatable deployment. LibreCellular will build on the work of numerous existing open source software and hardware projects, related to both the #cellular platform itself and associated test #infrastructure. Where necessary additional components will be developed, with any software source code and #hardware designs published under #opensource licences. The focus is very much on integration, testing, packaging and documentation, reusing and building upon existing solutions.. #LibreCellular#CellulaireLibre

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#4g #networks #sdr #documentation #cellular #infrastructure #hardware #opensource #librecellular #cellulairelibre