• Corpus ID: 17088443

DeepLearningKit - an GPU Optimized Deep Learning Framework for Apple's iOS, OS X and tvOS developed in Metal and Swift

@article{Tveit2016DeepLearningKitA,
  title={DeepLearningKit - an GPU Optimized Deep Learning Framework for Apple's iOS, OS X and tvOS developed in Metal and Swift},
  author={Amund Tveit and Torbj{\o}rn Morland and Thomas Brox R{\o}st},
  journal={ArXiv},
  year={2016},
  volume={abs/1605.04614}
}
In this paper we present DeepLearningKit - an open source framework that supports using pretrained deep learning models (convolutional neural networks) for iOS, OS X and tvOS. DeepLearningKit is developed in Metal in order to utilize the GPU efficiently and Swift for integration with applications, e.g. iOS-based mobile apps on iPhone/iPad, tvOS-based apps for the big screen, or OS X desktop applications. The goal is to support using deep learning models trained with popular frameworks such as… 

Figures from this paper

Deep Learning on Mobile and Embedded Devices

TLDR
This survey conducts an in-depth survey on important compression and acceleration techniques that help adapt deep learning models to mobile and embedded devices, which are specifically classify as pruning, quantization, model distillation, network design strategies, and low-rank factorization.

Deep Learning Acceleration Techniques for Real Time Mobile Vision Applications

Deep Learning (DL) has become a crucial technology for Artificial Intelligence (AI). It is a powerful technique to automatically extract high-level features from complex data which can be exploited

Deep Learning for Mobile Multimedia

TLDR
The state of the art in this exciting research area is reported, looking back to the evolution of neural networks, and arriving to the most recent results in terms of methodologies, technologies, and applications for mobile environments.

An Efficient Data Protection Architecture Based on Fragmentation and Encryption

TLDR
A completely revisited data protection scheme based on selective encryption based on lossless Discrete Wavelet Transform (DWT) that provides strong level of protection and good performance at the same time plus flexible storage dispersion schemes is presented.

Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence

TLDR
A comprehensive survey of the recent techniques and strategies developed to overcome resource challenges in pervasive AI systems and a deep literature review of communication-efficient techniques of distributed training and inference across the combination of IoT devices, edge devices and cloud servers.

Real-time illumination and shadow invariant lane detection on mobile platform

TLDR
Experimental results show that the proposed lane detection method is able to provide shadow, illumination and road defects invariant performance compared to the existing methods.

References

SHOWING 1-10 OF 34 REFERENCES

Fast Convolutional Nets With fbfft: A GPU Performance Evaluation

We examine the performance profile of Convolutional Neural Network training on the current generation of NVIDIA Graphics Processing Units. We introduce two new Fast Fourier Transform convolution

Deep Learning with Limited Numerical Precision

TLDR
The results show that deep networks can be trained using only 16-bit wide fixed-point number representation when using stochastic rounding, and incur little to no degradation in the classification accuracy.

Text Understanding from Scratch

TLDR
It is shown that temporal ConvNets can achieve astonishing performance without the knowledge of words, phrases, sentences and any other syntactic or semantic structures with regards to a human language.

Deep Learning in Speech Recognition

  • K. Iso
  • Environmental Science
  • 2017
TLDR
“Deep Learning for Speech Recognition and Related Applications”と題したワーク ショップをNIPSと併催で行っていることから,ほかの �“用分野に先駆けていた”.

Working with Metal - Advanced

  • Apple WWDC, published online - https://developer.apple.com/videos/wwdc/2014/,
  • 2014

Metal Shading Language Guide. published online https://developer.apple.com/ library/ios/documentation/Metal/Reference/MetalShadingLanguageGuide/ Introduction/Introduction

  • Metal Shading Language Guide. published online https://developer.apple.com/ library/ios/documentation/Metal/Reference/MetalShadingLanguageGuide/ Introduction/Introduction
  • 2015

Example of Sharing Memory Between GPU and CPU with Swift and Metal for iOS8

  • Memkite, published online http://memkite.com/blog/2014/12/30/ example-of-sharing-memory-between-gpu-and-cpu-with-swift-and-metal-for-ios8,
  • 2015

MetalKit Framework Reference. published online https://developer. apple.com/library/prerelease/ios/documentation/MetalKit/Reference/ MTKFrameworkReference/index

  • MetalKit Framework Reference. published online https://developer. apple.com/library/prerelease/ios/documentation/MetalKit/Reference/ MTKFrameworkReference/index
  • 2015

Why are Eight Bits Enough for Deep Neural Networks

  • 2015