ncnn is a high-performance neural network inference framework optimized for the mobile platform
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Updated
Jun 7, 2024 - C++
ncnn is a high-performance neural network inference framework optimized for the mobile platform
Up to 200x Faster Inner Products and Vector Similarity — for Python, JavaScript, Rust, C, and Swift, supporting f64, f32, f16 real & complex, i8, and binary vectors using SIMD for both x86 AVX2 & AVX-512 and Arm NEON & SVE 📐
A modern C++17 glTF 2.0 library focused on speed, correctness, and usability
RV: A Unified Region Vectorizer for LLVM
Single Header Quite Fast QOI(Quite OK Image Format) Implementation written in C++20
Simple neural network microkernels in C accelerated with ARMv8.2-a Neon vector intrinsics.
Pipelined lowlevel implementation of COLM for ARM-based systems
Colorful Mandelbrot set renderer in C# + OpenGL + ARM NEON
Hardkernel Odroid HC4 Ubuntu 20.04LTS install tutorial & tool build
benchmark for embededded-ai deep learning inference engines, such as NCNN / TNN / MNN / TensorFlow Lite etc.
FeatherCNN is a high performance inference engine for convolutional neural networks.
ncnn is a high-performance neural network inference framework optimized for the mobile platform
MACE is a deep learning inference framework optimized for mobile heterogeneous computing platforms.
Heterogeneous Run Time version of Caffe. Added heterogeneous capabilities to the Caffe, uses heterogeneous computing infrastructure framework to speed up Deep Learning on Arm-based heterogeneous embedded platform. It also retains all the features of the original Caffe architecture which users deploy their applications seamlessly.
A sample tutorial android NDK app for comparing Neon Architecture for doing various Image Manipulation like Gaussian Blur.
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