This presentation covers two uses cases using OpenPOWER Systems 1. Diabetic Retinopathy using AI on NVIDIA Jetson Nano: The objective is to classify the diabetic level solely on retina image in a remote area with minimum doctor's inference. The model uses VGG16 network architecture and gets trained from scratch on POWER9. The model was deployed on the Jetson Nano board. 1. Classifying Covid positivity using lung X-ray images: The idea is to build ML models to detect positive cases using X-ray images. The model was trained on POWER9, and the application was developed using Python.

1. Developing Deep Learning Applications on
OpenPOWER
Dr. Abhinandan S Prasad
Associate Prof, NIE Mysuru, India

2. AGENDA
• Introduction
• Diabetic Retinopathy
• Covid-19
2

3. 3
Introduction to Deep Learning Application
Development

4. INTRODUCTION
4
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5. INTRODUCTION
5
https://developer.nvidia.com/deep-learning-software

6. INTRODUCTION
6
https://en.wikipedia.org/wiki/Cloud_computing

7. 7
Diabetic Retinopathy using AI
Jetson Nano

8. EDGE COMPUTING
8
Fig. 1: An illustration of edge computing
resources to gain AI insight. Notably, edge intelligence has
garnered much attention from both the industry and academia.
on edg
In t
for ar
learnin
A. Ar
Wh
tremen
coined
intelli
do. T
refer f
techno
ligenc
https://arxiv.org/abs/1905.10083

9. EDGE AI
9

10. EDGE AI
10
https://becominghuman.ai/ai-vision-edge-computing-and-5g-1b0e49dba20e

11. EDGE DEVICES
11
Google Edge TPU
Jetson Nano Boards
https://developer.nvidia.com/embedded/jetson-nano-developer-kit
https://cloud.google.com/edge-tpu/

12. INTRODUCTION
• Diabetic Retinopathy is the field to diagnose the
level of diabetes based on the retina image
• There are five categories of diabetes
• No DR
• Mild
• Moderate
• Severe
• Proliferative DR
• Goal: Can we classify the diabetic level solely on
retina image?
12

13. SOLUTION OVERVIEW
13
1.DR High
Resolutio
n Labeled
Image set
3.Power9
System
model
training
2.Image
Preprocessing
and Data
Augmentation
4.Trained
Model
5.Inferencing
output from the
trained model on
Jetson Nano
6.Retina
Image
Capturing
Device
Feed
to
train
the
mod
el
agai
n
with
new
imag
es
Inferencing could be done on cloud, sending
image to cloud or model could itself be
deployed on mobile device to do the inference

14. SOLUTION OVERVIEW
• High resolution image date set has been taken with
around 35000 images and labelled
• Data pre-processing: Image rotation
• Hardware: POWER9 systems with 4 Tesla V100
SXM2 GPU’s
• Generated models are deployed on Jetson NANO
board
• Retina image capturing device captures the image
and send the image to cloud or the local device
which runs the model, It also sends images with
marking label to Power9 system
14

15. IMPLEMENTATION
• Convolutional Neural Network model has been
created trained on 35000 labelled retina images
• Model uses VGG16 network architecture and get
trained from scratch on Power9
• Original 35000 images are augmented to 1L images
out which 20% images are validation set images
used to validate the training accuracy during
training
• Model is trained till 50 epochs to get around 97%
accuracy
• Notebook code could be accessed at
https://github.com/arshad2101/retinopathy/blob/
master/Retinopathy_VGG16.ipynb
15

16. 16
Covid-19

17. MOTIVATION
• Corona virus is one of the most deadly virus with
highest number of causalities across the world
• We don’t have the enough mechanism to detect
the Corona from the X-Ray images
• Idea: Can we build ML models to detect positive
cases using X-ray images?
17

18. MOTIVATION
18

19. APPLICATION FLOW
19
C
UI in Python Flask or DJango Server in Python Flask
User enters
credentials
Authentication
happens at UI
end only
Inference result and base64 encoding of the image
C Model made
inference and
send back result
C
User uploads image
Image to
server
Inference result on
UI
C