Search Results for "arm"

whisper.cpp is a lightweight, C/C++ reimplementation of OpenAI’s Whisper automatic speech recognition (ASR) model—designed for efficient, standalone transcription without external dependencies. The entire high-level implementation of the model is contained in whisper.h and whisper.cpp. The rest of the code is part of the ggml machine learning library. The command downloads the base.en model converted to custom ggml format and runs the inference on all .wav samples in the folder samples....

...Implemented primarily in C and C++, it is designed to operate with minimal external dependencies while taking advantage of hardware-specific acceleration technologies such as ARM NEON and x86 AVX2 instructions. The system supports multiple optimization techniques including quantization, pruning, and speculative decoding to improve performance while reducing computational overhead. It also provides tools to convert models from popular formats like PyTorch checkpoints into optimized runtime formats that can be executed on supported hardware platforms.

tvm-cn is a community-driven project that provides Chinese documentation for the Apache TVM deep learning compiler stack. Apache TVM is an open-source system designed to optimize and deploy machine learning models efficiently across different hardware platforms such as CPUs, GPUs, and ARM devices. The goal of the repository is to centralize translated learning materials and technical documentation so that Chinese-speaking developers can study the TVM ecosystem more easily. The project translates official TVM guides and organizes them into structured documentation that explains how to compile, optimize, and deploy deep learning models on heterogeneous hardware architectures. ...

...This approach allows developers to build machine learning models using standard frameworks and then deploy them to devices with extremely limited memory and processing power. The runtime library is intentionally lightweight and optimized for platforms such as Arm Cortex-M microcontrollers, making it suitable for real-time edge applications.

The Whole-Body Control framework jointly developed at Stanford University and The University of Texas at Austin provides advanced control for fixed base manipulators and is currently running on the the Meka A2 Arm and the Dreamer/Meka Humanoid robot. The code repository is hosted on Github, please go to https://github.com/poftwaresatent/stanford_wbc

Java Program of a machine which plays chess: it recognizes the chessboard, the position, the move of the opponent, and asks an external Chess Engine to reply to the opponent move. it is connected to a real robot which has an arm to manipulate the pieces.