Displaying 1 to 20 from 42 results

Forge - A neural network toolkit for Metal

  •    Swift

Forge is a collection of helper code that makes it a little easier to construct deep neural networks using Apple's MPSCNN framework. Conversion functions. MPSCNN uses MPSImages and MTLTextures for everything, often using 16-bit floats. But you probably want to work with Swift [Float] arrays. Forge's conversion functions make it easy to work with Metal images and textures.

tvm - bring deep learning workloads to bare metal

  •    C++

TVM is a Tensor intermediate representation(IR) stack for deep learning systems. It is designed to close the gap between the productivity-focused deep learning frameworks, and the performance- and efficiency-focused hardware backends. TVM works with deep learning frameworks to provide end to end compilation to different backends. Checkout our announcement for more details.© Contributors, 2017. Licensed under an Apache-2.0 license.

nnvm - Bring deep learning to bare metal

  •    C++

The following code snippet demonstrates the general workflow of nnvm compiler.Licensed under an Apache-2.0 license.

ARKit-Sampler - Code examples for ARKit.

  •    Swift

ARKit-Sampler is a collection of ARKit samples. A simple AR with 3 lines code.




glsl-optimizer - GLSL optimizer based on Mesa's GLSL compiler

  •    C++

A C++ library that takes GLSL shaders, does some GPU-independent optimizations on them and outputs GLSL or Metal source back. Optimizations are function inlining, dead code removal, copy propagation, constant folding, constant propagation, arithmetic optimizations and so on. Apparently quite a few mobile platforms are pretty bad at optimizing shaders; and unfortunately they also lack offline shader compilers. So using a GLSL optimizer offline before can make the shader run much faster on a platform like that. See performance numbers in this blog post.

Bender - Easily craft fast Neural Networks on iOS! Use TensorFlow models. Metal under the hood.

  •    Swift

Bender is an abstraction layer over MetalPerformanceShaders useful for working with neural networks. Bender is an abstraction layer over MetalPerformanceShaders which is used to work with neural networks. It is of growing interest in the AI environment to execute neural networks on mobile devices even if the training process has been done previously. We want to make it easier for everyone to execute pretrained networks on iOS.

bgfx - Cross-platform, graphics API agnostic, "Bring Your Own Engine/Framework" style rendering library

  •    C++

Cross-platform, graphics API agnostic, "Bring Your Own Engine/Framework" style rendering library. http://airmech.com/ AirMech is a free-to-play futuristic action real-time strategy video game developed and published by Carbon Games.


The-Forge - The Forge Cross-Platform Rendering Framework PC, Linux, Ray Tracing, macOS / iOS, Android, XBOX, PS4

  •    C++

The intended usage of The Forge is to enable developers to quickly build their own game engines. The Forge can provide the rendering layer for custom next-gen game engines. Added a unified input system based on Gainput to all platforms (https://github.com/jkuhlmann/gainput). The new input system substantially simplified input management on the application level over all platforms. We also simplified the camera controller. Added also new VirtualJoystick class in UI.

FlexibleImage - A simple way to play with the image!

  •    Swift

FlexibleImage is implemented with the hope that anyone could easily develop an app that provides features such as Camera Filter and Theme. When you write code in the "Method Chaining" style, the effect is applied in the appropriate order. You may want to see Examples section first if you'd like to see the actual code.

TensorFlow-iOS-Example - Source code for my blog post "Getting started with TensorFlow on iOS"

  •    Swift

This is the code that accompanies my blog post Getting started with TensorFlow on iOS. It uses TensorFlow to train a basic binary classifier on the Gender Recognition by Voice and Speech Analysis dataset.

YOLO-CoreML-MPSNNGraph - Tiny YOLO for iOS implemented using CoreML but also using the new MPS graph API

  •    Swift

This is the source code for my blog post YOLO: Core ML versus MPSNNGraph. YOLO is an object detection network. It can detect multiple objects in an image and puts bounding boxes around these objects. Read my other blog post about YOLO to learn more about how it works.

BrainCore - The iOS and OS X neural network framework

  •    Swift

BrainCore is a simple but fast neural network framework written in Swift. It uses Metal which makes it screamin' fast. If you want to see it in action check out InfiniteMonkeys—an app that uses a recursive neural network to generate poems. When splitting, the inputSize of the target layers will determine where to split. If the sum of the target layers' inputSizes doesn't match the source layer's outputSize and error will be thrown.

Water - Simple calculation to render cheap water effects.

  •    Swift

Simple calculation to render cheap water effects. Choose to run OSX or iOS version.

BNNS-vs-MPSCNN - Compares the speed of Apple's two deep learning frameworks: BNNS and Metal Performance Shaders

  •    Swift

This app compares the speed of Apple's two deep learning frameworks: BNNS and Metal Performance Shaders (MPSCNN). It creates a basic convolutional neural network with 2 convolutional layers, 2 pooling layers, and a fully-connected layer. Then it measures how long it takes to sends the same image 100 times through the network.

metalbrot-playground - An interactive playground showing how to use Metal compute kernels.

  •    Swift

Metalbrot.playground is an interactive playground showing how to use Metal compute kernels with Swift. More information can be found on my blog.

NabaztagHackKit - A simple SDK to get your hands dirty with Nabaztag

  •    Ruby

Everything you need to hack the Rabbit: a sinatra server including simple api framework to run custom bytecode on Nabaztag v1/v2. Includes original compiler sources for linux and a modified mac os x version. The Hack Kit is distributed as a ruby gem. It comes with a simple web server (based on sinatra) which runs out-of-the for connecting you rabbit and distributing the nabaztag bytecode. In addition it includes sinatra helpers/modules to communicate with the rabbit easily. Lastly it provides binaries to compile your own Nabaztag bytecode (see Binaries below).





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