MACE is a deep learning inference framework optimized for mobile heterogeneous computing platforms.
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Updated
May 28, 2019 - C++
MACE is a deep learning inference framework optimized for mobile heterogeneous computing platforms.
Colorful Mandelbrot set renderer in C# + OpenGL + ARM NEON
Pipelined lowlevel implementation of COLM for ARM-based systems
Simple neural network microkernels in C accelerated with ARMv8.2-a Neon vector intrinsics.
A sample tutorial android NDK app for comparing Neon Architecture for doing various Image Manipulation like Gaussian Blur.
ncnn is a high-performance neural network inference framework optimized for the mobile platform
Hardkernel Odroid HC4 Ubuntu 20.04LTS install tutorial & tool build
Single Header Quite Fast QOI(Quite OK Image Format) Implementation written in C++20
RV: A Unified Region Vectorizer for LLVM
Heterogeneous Run Time version of TensorFlow. Added heterogeneous capabilities to the TensorFlow, 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 TensorFlow architecture which users deploy their applications seamlessly.
benchmark for embededded-ai deep learning inference engines, such as NCNN / TNN / MNN / TensorFlow Lite etc.
Heterogeneous Run Time version of MXNet. Added heterogeneous capabilities to the MXNet, 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 MXNet architecture which users deploy their applications seamlessly.
A modern C++17 glTF 2.0 library focused on speed, correctness, and usability
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 📐
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.
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