Search Results for "performance"

...The project bundles runnable samples that show how to run TensorFlow Lite/Edge TPU models (and similar lightweight runtimes) on mobile and embedded platforms, demonstrating common tasks like image classification, object detection, audio recognition, and pose estimation. Each sample is intended to be both a learning aid and a practical starting point: code is organized to show model loading, pre/post-processing, performance measurement, and common optimization knobs (quantization, NNAPI/Delegate usage, and hardware accelerators). The repo also collects small, well-documented models and conversion scripts so developers can reproduce a pipeline from a full-size model down to a device-friendly artifact.

LiteRT-LM is Google’s open-source inference framework for deploying large language models on edge devices. It is built for production-oriented local LLM execution across Android, iOS, desktop, web, embedded, and IoT environments. The framework focuses on performance, hardware acceleration, and efficient model serving close to the user instead of relying only on remote cloud inference. It supports CPU execution across major platforms and adds GPU or NPU acceleration where available. LiteRT-LM is especially relevant for developers building private, low-latency AI features on phones, laptops, Raspberry Pi-style devices, and other edge hardware. ...

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 distribution, security risk control. ...

TNN, a high-performance, lightweight neural network inference framework open sourced by Tencent Youtu Lab. It also has many outstanding advantages such as cross-platform, high performance, model compression, and code tailoring. The TNN framework further strengthens the support and performance optimization of mobile devices on the basis of the original Rapidnet and ncnn frameworks.