#Huawei

Bolt, a library for efficient neural network deployment, offers over 15% speed improvement compared to other open-source libraries. It facilitates model conversion from Caffe, ONNX, TFLite, and TensorFlow, supporting multiple inference precisions such as FP32 and 1-BIT. Bolt's compatibility spans various platforms, including ARM and X86 CPUs and GPUs from diverse vendors. It pioneers support for NLP alongside CV applications, featuring advanced graph optimization, efficient thread settings, and algorithm tuning. Extensively used in HUAWEI departments, Bolt is a reliable tool enhancing performance in CV, NLP, and recommendation applications.

Browse the Noah's Ark Lab open-source repository featuring a variety of research-oriented subprojects housed in specific directories. Ideal for developers seeking to explore machine learning models such as mRNN-mLSTM, this collection, although not officially supported by Huawei, offers a wealth of resources. Participate by referencing subproject names in issues or PR titles as per the recommended format and explore diverse AI solutions in this inclusive research platform.

OpenHarmony is a distributed smart operating system based on a microkernel architecture, developed by Huawei for broad industry adoption. Launched in 2019, it supports a variety of devices including smartphones, tablets, and smart screens, and extends into sectors like automotive and industrial applications. OpenHarmony focuses on modular integration and flexible deployment across different hardware environments. It continuously evolves with significant updates, supporting both native and Android-compatible applications. The system competes alongside major operating systems, supported by widespread developer engagement, a growing ecosystem, and backing from global tech communities.

HEBO is an innovative Bayesian optimization library by Huawei Noah's Ark Lab, crafted for a wide range of applications. As a notable participant in the NeurIPS 2020 Black-Box Optimization Challenge, it efficiently optimizes complex functions using heteroscedastic evolutionary methods. This library offers a flexible framework that simplifies implementation and integration into existing workflows, making it a valuable resource for researchers and developers in Bayesian optimization.