Embedded and mobile deep learning research notes

1. DeepMon: Mobile GPU-based Deep Learning Framework for Continuous Vision Applications [MobiSys '17]

2. DeepEye: Resource Efficient Local Execution of Multiple Deep Vision Models using Wearable Commodity Hardware [MobiSys '17]

3. MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU [EMDL '17]

4. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications [arXiv '17, Google ]

5. Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding [ICLR'16 (oral)]

6. Quantized Convolutional Neural Networks for Mobile Devices [CVPR '16]

7. DeepSense: A GPU-based deep convolutional neural network framework on commodity mobile devices [WearSys '16]

8. DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices [IPSN '16]

9. EIE: Efficient Inference Engine on Compressed Deep Neural Network [ISCA '16]

10. MCDNN: An Approximation-Based Execution Framework for Deep Stream Processing Under Resource Constraints [MobiSys '16]

11. DXTK: Enabling Resource-efficient Deep Learning on Mobile and Embedded Devices with the DeepX Toolkit [MobiCASE '16]

12. Sparsification and Separation of Deep Learning Layers for Constrained Resource Inference on Wearables [SenSys ’16]

13. An Early Resource Characterization of Deep Learning on Wearables, Smartphones and Internet-of-Things Devices [IoT-App ’15]

14. CNNdroid: GPU-Accelerated Execution of Trained Deep Convolutional Neural Networks on Android [MM '16]

1. Squeezing Deep Learning Into Mobile Phones

2. Deep Learning – Tutorial and Recent Trends

3. Efficient Convolutional Neural Network Inference on Mobile GPUs

4. Deep learning systems, UW course schedule(focused on systems design, not learning)

1. ARM-software/ComputeLibrary: The ARM Computer Vision and Machine Learning library is a set of functions optimised for both ARM CPUs and GPUs using SIMD technologies, Intro

2. Apple CoreML

3. Microsoft Embedded Learning Library

1. SaschaWillems/Vulkan: Examples and demos for the new Vulkan API

2. ARM-software/vulkan-sdk: ARM Vulkan SDK

3. alexhultman/libvc: Vulkan Compute for C++ (experimentation project)

4. Deep Learning in a Single File for Smart Devices — mxnet

5. TensorFlow Android Camera Demo

6. bwasti/AICamera: Demonstration of using Caffe2 inside an Android application.

7. mtmd/Mobile_ConvNet: RenderScript based implementation of Convolutional Neural Networks for Android phones

8. harvardnlp/nmt-android: Neural Machine Translation on Android

9. hollance/TensorFlow-iOS-Example: Source code for my blog post "Getting started with TensorFlow on iOS"

1. ARM® Mali™ GPU OpenCL Developer Guide, pdf

2. Optimal Compute on ARM MaliTM GPUs

3. GPU Compute for Mobile Devices

4. Compute for Mobile Devices Performance focused

5. Hands On OpenCL

6. Adreno OpenCL Programming Guide

7. Better OpenCL Performance on Qualcomm Adreno GPU

1. mil-tokyo/webdnn: Fastest DNN Execution Framework on Web Browser

1. Bifrost GPU architecture and ARM Mali-G71 GPU

2. Midgard GPU Architecture, ARM Mali-T880 GPU

3. Mobile GPU market share

1. [Adreno] csarron/qcom_vendor_binaries: Common Proprietary Qualcomm Binaries
2. [Mali] Fevax/vendor_samsung_hero2ltexx: Blobs from s7 Edge G935F