XNNPACK
High-efficiency floating-point neural network inference operators for mobile, server, and Web (by google)
ncnn
ncnn is a high-performance neural network inference framework optimized for the mobile platform (by Tencent)
| XNNPACK | ncnn | |
|---|---|---|
| 8 | 12 | |
| 1,700 | 19,234 | |
| 1.6% | 1.0% | |
| 9.9 | 9.4 | |
| 6 days ago | 7 days ago | |
| C | C++ | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
XNNPACK
Posts with mentions or reviews of XNNPACK.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-05-02.
- Xnnpack: High-efficiency floating-point neural network inference operators
- Can a NPU be used for vectors?
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Performance critical ML: How viable is Rust as an alternative to C++
Why are you writing your own inference code in C++ or Rust instead of using some kind of established framework like XNNPACK?
- [P] Pure C/C++ port of OpenAI's Whisper
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[Discussion] Is XNNPACK a part of mediapipe? or should be additionally configured with mediapipe?
XNNPACK - https://github.com/google/XNNPACK
- WebAssembly Techniques to Speed Up Matrix Multiplication by 120x
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Prediction: Macs won't see many new games, no matter how powerful their hardware is
Ok, concrete example time! At work, we're going to be using some software which includes XNNPACK, which is a library of highly-optimised operations for doing neural-network inference. This is the sort of thing where people have gone in and specifically tuned for performance, and nope, there's no attempt at all made to have code which is different for Intel/AMD or Apple/Other ARM. What they target is elements of the ISA, like NEON (i.e. ARM SIMD) and SSE, AVX etc. on x86(-64). And Wasm SIMD for Wasm.
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Where are Nvidia's DLSS models stored and how big are they?
It's quite simple. https://github.com/google/XNNPACK for example.
ncnn
Posts with mentions or reviews of ncnn.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-02-12.
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AMD Funded a Drop-In CUDA Implementation Built on ROCm: It's Open-Source
ncnn uses Vulkan for GPU acceleration, I've seen it used in a few projects to get AMD hardware support.
https://github.com/Tencent/ncnn
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[D] Best way to package Pytorch models as a standalone application
They're using NCNN to package the model. Have a look. https://github.com/Tencent/NCNN
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Realtime object detection android app
Hi. Here is my prefered android app for realtime objet detection: https://github.com/nihui/ncnn-android-nanodet ; https://github.com/Tencent/ncnn contains a lot of android demo app for a lot of models.
- ncnn: High-performance neural network inference framework optimized for mobile
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Esp32 tensorflow lite
ncnn home page: https://github.com/Tencent/ncnn
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MMDeploy: Deploy All the Algorithms of OpenMMLab
ncnn
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Draw Things, Stable Diffusion in your pocket, 100% offline and free
Yes, Android devices tend to have bigger RAMs, making running 1024x1024 possible (this is not possible at all on iPhones, which could peak around 5GiB memory with my current implementation, some serious engineering required to bring that down on iPhone devices). The problem is I am not sure about speed. I would likely switch to NCNN (https://github.com/Tencent/ncnn) as the backend which have a decent Vulkan computing kernel support. It is definitely a possibility and there is a path to do that.
- What’s New in TensorFlow 2.10?
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[Technical Article] OCR Upgrade
As the leading open-source inference framework in China and in the world, what we like are its almost zero cost cross-platform capability, high inference speed, and minimal deployment volume. (Project address: https://github.com/Tencent/ncnn)
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Is there a functioning neural netowork or backbone written in pure C language only?
If you’re not planning on training the neural net on an embedded device and just do inference, this might interest you: https://github.com/Tencent/ncnn
What are some alternatives?
When comparing XNNPACK and ncnn you can also consider the following projects:
gemm-benchmark - Simple [sd]gemm benchmark, similar to ACES dgemm
rife-ncnn-vulkan - RIFE, Real-Time Intermediate Flow Estimation for Video Frame Interpolation implemented with ncnn library
cpuid2cpuflags - Tool to generate CPU_FLAGS_* for your CPU
deepdetect - Deep Learning API and Server in C++14 support for Caffe, PyTorch,TensorRT, Dlib, NCNN, Tensorflow, XGBoost and TSNE
wasmblr - C++ WebAssembly assembler in a single header file
netron - Visualizer for neural network, deep learning and machine learning models
Genann - simple neural network library in ANSI C
darknet - Convolutional Neural Networks
ruby-fann - Ruby library for interfacing with FANN (Fast Artificial Neural Network)
RPi_64-bit_Zero-2-image - Raspberry Pi Zero 2 W 64-bit OS image with OpenCV, TensorFlow Lite and ncnn Framework.
HIP-CPU - An implementation of HIP that works on CPUs, across OSes.
torch-mlir - The Torch-MLIR project aims to provide first class support from the PyTorch ecosystem to the MLIR ecosystem.