mlir-aie
ncnn
| mlir-aie | ncnn | |
|---|---|---|
| 1 | 12 | |
| 231 | 19,310 | |
| 8.7% | 1.4% | |
| 9.8 | 9.4 | |
| 4 days ago | 3 days ago | |
| MLIR | C++ | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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.
mlir-aie
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AMD Receives Approval for Acquisition of Xilinx
>come up with a "better" (performant, cheaper, easier to use, etc.) solution than GPUs for ML applications
you probably are aware but Xilinx themselves is attempting this with their versal aie boards which (in spirit) similar to GPUs, in that they group together a programmable fabric of programmable SIMD type compute cores.
https://www.xilinx.com/support/documentation/architecture-ma...
i have not played with one but i've been told (by a xilinx person, so grain of salt) the flow from high-level representation to that arch is more open
https://github.com/Xilinx/mlir-aie
ncnn
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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?
torch-mlir - The Torch-MLIR project aims to provide first class support from the PyTorch ecosystem to the MLIR ecosystem.
XNNPACK - High-efficiency floating-point neural network inference operators for mobile, server, and Web
circt - Circuit IR Compilers and Tools
rife-ncnn-vulkan - RIFE, Real-Time Intermediate Flow Estimation for Video Frame Interpolation implemented with ncnn library
heavydb - HeavyDB (formerly OmniSciDB)
deepdetect - Deep Learning API and Server in C++14 support for Caffe, PyTorch,TensorRT, Dlib, NCNN, Tensorflow, XGBoost and TSNE
iree - A retargetable MLIR-based machine learning compiler and runtime toolkit.
netron - Visualizer for neural network, deep learning and machine learning models
concrete - Concrete: TFHE Compiler that converts python programs into FHE equivalent
darknet - Convolutional Neural Networks
RPi_64-bit_Zero-2-image - Raspberry Pi Zero 2 W 64-bit OS image with OpenCV, TensorFlow Lite and ncnn Framework.