Real-Time Image Recognition Using Collaborative IoT Devices

ACM ReQuEST workshop co-located with ASPLOS 2018

This repository contains demo files for demonstration of Musical Chair[1] applied on two state-of-art deep learning neural networks, AlexNet[2] and VGG16[3].

Installation

Please make sure that you have Python 2.7 running on your device. We have two versions of model inference. One is using GPU and running model inference on single machine. Another is using CPU and using RPC to off-shore the computation to other devices. We will have different installation guide for those two versions model inference.

Single device (GPU and CPU).

(This is NVidia Jetson TX2 version in our paper)

Dependencies:

• tensorflow-gpu >= 1.5.0
• Keras >= 2.1.3

pip install keras

Please refer to official installation guideline from Keras for more information

Multiple devices (CPU and RPC).

(This is Raspberry PI 3 versions in our paper)

Dependencies:

• tensorflow >= 1.5.0
• Keras >= 2.1.3
• avro >= 1.8.2

We have provided dependency file here. You can execute this file to install packages.

pip install -r requirements.txt

Quick Start

Single device (GPU and CPU)

(This is NVidia Jetson TX2 version in our paper)

GPU Version

Execute predict file to run model inference.

python predict.py

CPU Version

CUDA_VISIBLE_DEVICES= python predict.py

Multiple devices (CPU and RPC)

(This is Raspberry PI 3 versions in our paper)

We make a checklist for you before running our program.

• Have all correct packages installed on Raspberry Pi.
• The Raspberry PI has port 12345, 9999 open.
• Put correct IP address in IP table file mutiple-devices/alexnet/resource/ip. The IP table file is in json format.

AlexNet

For AlexNet, we have same model partition, so we will use the same node file for different system setup. The IP table is default to 4 devices setup. You need to add 1 more IP address to block1 if you want to test 6 devices setup.

• On all of your device except the initial sender, run the node.

python node.py

• Start the data sender. You should be able to see console log.

python initial.py

• If you modify our code, you can use flag to debug.

python node.py -d

VGG16

For VGG16, we have different model separation for different system setup, so we put two directories under mutiple-devices/vgg16. For 8devices, you should have 3 devices for block234 and 2 devices for fc1, which means you need 2 IP addresses for those 2 blocks in IP table. For 11devices, you should have 7 devices for block12345, so put 7 IP addresses at IP table.

• On all of your device except the initial sender, run the node.

python node.py

• Start the data sender. You should be able to see console log.

python initial.py

Refereces

[1]: R. Hadidi, J. Cao, M. Woodward, M. Ryoo, and H. Kim, "Musical Chair: Efficient Real-Time Recognition Using Collaborative IoT Devices," ArXiv e-prints:1802.02138.

[2]: A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet Classification With Deep Convolutional Neural Networks}," in Advances in Neural InformationProcessing Systems (NIPS), pp. 1097--1105, 2012.

[3]: K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," in International Conference onLearning Representations (ICLR), 2015.