Search Results for "linux device driver development"
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Showing 8 open source projects for "linux device driver development"
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LiteRT
LiteRT, successor to TensorFlow Lite
LiteRT is Google's next-generation on-device machine learning framework and the successor to TensorFlow Lite, designed for high-performance AI and generative AI deployment across edge devices. It provides efficient model conversion, optimization, and runtime execution while leveraging hardware acceleration from CPUs, GPUs, and NPUs. LiteRT supports a wide range of platforms, including Android, iOS, Linux, macOS, Windows, web environments, and IoT devices. -
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RunAnywhere
Production ready toolkit to run AI locally
RunAnywhere SDKs are a set of cross-platform development tools that enable applications to run artificial intelligence models directly on user devices instead of relying on cloud infrastructure. The toolkit allows developers to integrate language models, speech recognition, and voice synthesis capabilities into mobile or desktop applications while keeping all computation local. By running models entirely on device, the platform eliminates network latency and protects user data because... -
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BrowserOS
Agentic browser; privacy-first alternative to ChatGPT Atlas
BrowserOS is an open-source, agentic web browser built on a Chromium base that integrates AI agents directly into the browsing experience. Rather than just doing standard browsing, it places AI intelligence at the core: you can connect your own API keys (for e.g., OpenAI, Anthropic, Google Gemini) or run local models (via e.g., Ollama) so that your browsing data and automation stay on your machine — privacy and control are emphasized throughout. The interface remains familiar to users of... -
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Operit AI
Powerful Android AI agent with tools, automation, and Linux shell
...It integrates deep system-level capabilities with a wide range of tools, allowing the AI to perform real tasks such as file management, automation, and system control directly on the device. A standout aspect of the project is its built-in Ubuntu 24 environment, which enables users to run Linux commands, scripts, and development tools in a mobile context. Operit supports both local and remote AI models, including offline execution through frameworks like llama.cpp and MNN, helping preserve user privacy while maintaining flexibility. ... -
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5
MNN
MNN is a blazing fast, lightweight deep learning framework
MNN is a highly efficient and lightweight deep learning framework. It supports inference and training of deep learning models, and has industry leading performance for inference and training on-device. At present, MNN has been integrated in more than 20 apps of Alibaba Inc, such as Taobao, Tmall, Youku, Dingtalk, Xianyu and etc., covering more than 70 usage scenarios such as live broadcast, short video capture, search recommendation, product searching by image, interactive marketing, equity... -
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MediaPipe Solutions
Cross-platform, customizable ML solutions
MediaPipe is an open-source framework developed by Google for building cross-platform machine learning pipelines that process audio, video, and other streaming data in real time. The system provides developers with tools and reusable components that allow them to combine multiple machine learning models with preprocessing and postprocessing logic into efficient perception pipelines. These pipelines can run on a wide variety of platforms including mobile devices, desktop systems, web... -
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OneFlow
OneFlow is a deep learning framework designed to be user-friendly
OneFlow is a deep learning framework designed to be user-friendly, scalable and efficient. An extension for OneFlow to target third-party compiler, such as XLA, TensorRT and OpenVINO etc.CUDA runtime is statically linked into OneFlow. OneFlow will work on a minimum supported driver, and any driver beyond. For more information. Distributed performance (efficiency) is the core technical difficulty of the deep learning framework. OneFlow focuses on performance improvement and heterogeneous... -
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TensorRT Pro
C++ library based on tensorrt integration
High-level interface for C++/Python. Simplify the implementation of the custom plugin. And serialization and deserialization have been encapsulated for easier usage. Simplify the compilation of fp32, fp16 and int8 for facilitating the deployment with C++/Python in server or embedded device. Models ready for use also with examples are RetinaFace, Scrfd, YoloV5, YoloX, Arcface, AlphaPose, CenterNet and DeepSORT(C++).
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