Search Results for "format low level"
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Showing 18 open source projects for "format low level"
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AsmJit
Low-latency machine code generation
...It is commonly used in applications such as emulators, compilers, and high-performance computing systems where runtime optimization is essential. asmjit emphasizes low latency and efficiency, ensuring that generated code executes quickly without significant overhead. Its modular design allows developers to integrate it into various systems with minimal friction. Overall, asmjit bridges the gap between high-level programming and low-level execution by enabling efficient runtime code generation. -
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whisper.cpp
Port of OpenAI's Whisper model in C/C++
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. whisper.cpp supports integer quantization of the Whisper ggml models. ... -
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tt-metal
TT-NN operator library, and TT-Metalium low level kernel programming
tt-metal, also referred to in its documentation as TT-Metalium, is Tenstorrent’s low-level software development kit for programming applications on Tenstorrent AI accelerators. The project is designed for developers who need direct access to the company’s Tensix processor architecture, exposing a programming model that is closer to hardware control than high-level inference frameworks. Instead of following a traditional GPU model centered on massive thread parallelism, the platform is built around a grid of specialized compute nodes called Tensix cores, each with local SRAM, dedicated compute units, and multiple RISC-V control processors. ... -
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TensorFlow
TensorFlow is an open source library for machine learning
...Nodes take tensors—multidimensional arrays—as input and produce tensors as output. The framework allows for these algorithms to be run in C++ for better performance, while the multiple levels of APIs let the user determine how high or low they wish the level of abstraction to be in the models produced. Tensorflow can also be used for research and production with TensorFlow Extended. -
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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ggml
Tensor library for machine learning
ggml is an open-source tensor library designed for efficient machine learning computation with a focus on running models locally and with minimal dependencies. Written primarily in C and C++, the library provides low-level tensor operations and automatic differentiation that allow developers to implement machine learning algorithms and neural networks efficiently. The project emphasizes portability and performance, enabling machine learning inference across a wide range of hardware environments including CPUs and specialized accelerators. It is widely used as a foundational component in projects that run large language models locally, including tools that perform inference for transformer-based models. ... -
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TensorFlow Serving
Serving system for machine learning models
...In order to serve a Tensorflow model, simply export a SavedModel from your Tensorflow program. SavedModel is a language-neutral, recoverable, hermetic serialization format that enables higher-level systems and tools to produce, consume, and transform TensorFlow models. -
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3FS
A high-performance distributed file system
The 3FS repository (standing likely for “Feature 3F System” or similar) is focused on providing a feature extraction and transformation framework tailored to deep and large models, especially in token-based systems. Its primary aim is to support efficient and scalable feature transformation pipelines—especially for inference environments—by batching, caching, and integrating feature-based modules like segmenters, sparse retrievers, and scorers seamlessly. The repo includes APIs to define... -
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CodeContests
Large dataset of coding contests designed for AI and ML model training
...The dataset is distributed in Riegeli format using Protocol Buffers, with separate training, validation, and test splits for reproducible machine learning experiments. -
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ML++
A library created to revitalize C++ as a machine learning front end
...Unfortunately, for C++ programmers and enthusiasts, there appears to be a lack of support in the field of machine learning. To fill that void and give C++ a true foothold in the ML sphere, this library was written. The intent with this library is for it to act as a crossroad between low-level developers and machine learning engineers. ML++, like most frameworks, is dynamic, and constantly changing. This is especially important in the world of ML, as new algorithms and techniques are being developed day by day. Here are a couple of things currently being developed for ML++. Call the optimizer that you would like to use. ... -
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SINGA
A distributed deep learning platform
...SINGA records the computation graph and applies the backward propagation automatically after forward propagation. The optimization of memory are implemented in the Device class. SINGA supports loading ONNX format models and saving models defined using SINGA APIs into ONNX format, which enables AI developers to use models across different libraries and tools. SINGA supports the time profiling of each of the operators buffered in the graph. Half precision is supported to bring benefits. -
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SmartOpenCV
OpenCV enhancement library for Android
SmartOpenCV is an enhanced computer vision toolkit built on top of OpenCV that aims to simplify and extend common image processing and computer vision tasks through higher-level abstractions and utilities. It provides pre-built modules and optimized pipelines for tasks such as object detection, image transformation, and feature extraction, reducing the need for low-level implementation. The framework is designed to be more developer-friendly than raw OpenCV by offering cleaner APIs and reusable components that accelerate development. ... -
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BWEM
Fast and robust map analyser for Brood War.
Brood War Easy Map is a C++ library that analyses Brood War's maps and provides relevant information such as areas, choke points and base locations. It is built on top of the BWAPI library. It first aims at simplifying the development of bots for Brood War, but can be used for any task requiring high level map information. It can be used as a replacement for the BWTA2 add-on, as it performs faster and shows better robustness while providing similar information. -
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Porcupine2 is a small wearable sensory unit for logging motion data and doing low-level activity recognition. The project includes electronic schematics for the hardware, software for the microcontroller and client-side software for hardware-interaction.
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Project ready to essay with OpenCv libraries, using QT. Created in Windows but possibly easy to compile in other OpenCv and QT compatible systems.
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An implementation of memory-prediction framework applied for image recognition. Based on Jeff Hawkins' book On Intelligence. It models the high-level hierarchical architecture of human neocortex and uses Bayesian belief revision for making predictions
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Easy-to-use platform-independent plugin API for the extraction of low-level features from audio data in PCM format, as required in the context of music information retrieval software.
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Panta Rhei will provides a platform for developing low-level locomotion and balance controllers for real-time applications.
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Monk Computer Vision
A low code unified framework for computer vision and deep learning
Monk is an open source low code programming environment to reduce the cognitive load faced by entry level programmers while catering to the needs of Expert Deep Learning engineers. There are three libraries in this opensource set. - Monk Classiciation- https://monkai.org. A Unified wrapper over major deep learning frameworks. Our core focus area is at the intersection of Computer Vision and Deep Learning algorithms
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