Internet of Things (IoT) and Artificial Intelligence (AI) role in Medical and Healthcare Systems
+ History of IoT
+ Internet of Nano Things (IoNT)
+ IoT and IoNT for Medical and Healthcare Systems
+ IoT and Artificial Intelligence (AI)
+ IoT and AI for Health
+ Deep Learning Accelerator
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Internet of Things (IoT) and Artificial Intelligence (AI) role in Medical and Healthcare Systems
1. Lecture Subject
Internet of Things (IoT) and AI Role in Medical
and Healthcare Systems
Somayyeh Jafarali Jassbi
PhD, Assistant Professor,
Head of Computer Engineering Department, SRBIAU
s.jassbi@srbiau.ac.ir
Hamidreza Bolhasani
PhD Candidate, Fonder & Data Scientist at DataBioX
hamidreza.bolhasani@srbiau.ac.ir
March 2022
2. - Internet of Things (IoT) History
- IoT Applications for Healthcare
- Internet of Nano Things (IoNT)
- IoNT Applications in Health and Medical
- IoT Systems Architecture Overview
- IoT and Artificial Intelligence (AI)
- AI / ML / DL Fundamental Concepts
- Deep Learning Accelerators
- Conclusion - Q & A
Table of Contents
3. Internet of Things (IoT)
1
Ref. Mahmuda Khatun Mishu et al. MDPI 2020. Prospective Ecient Ambient Energy Harvesting
Sources for IoT-Equipped Sensor Applications
4. IoT for Healthcare
2
Ref.
[1] Yazdan Ahmad Qadri et al. IEEE COMST 2020
[2] Kyeonghye Guk et al. MDPI, Nanomaterial, 2019.
15. Deep Neural Networks (DNN)
Ref. B. Reagen, R. Adolf, P. N. Whatmough, G. Wei, and D. M. Brooks, Deep Learning for Computer Architects,
ser. Synthesis Lectures on Computer Architecture. Morgan & Claypool Publishers, 2017.
13
28. Convolutional Neural Networks (CNNs)
Ref. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in
International Conference on Learning Representations (ICLR), 2015. 26
29. Convolutional Neural Networks (CNNs)
Ref. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in
International Conference on Learning Representations (ICLR), 2015.
AlexNet
VGG16
27
30. Histopathologic Images CT-Scan MRI PET-Scan
What does a Deep Neural Network needs to be Trained?
BIG DATA
How much data?
So much that we get our desired results!
28
31. Our First Contribution
DataBioX founded in 2020 for sharing biomedical datasets.
DataBioX first dataset: Invasive Ductal Carcinoma Grading Dataset
29
35. Published Paper in 2020
Elsevier Publication
Officially requested by many prestigious
Universities around the world like MIT and
University of Johns Hopkins.
33
42. Ref. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in
International Conference on Learning Representations (ICLR), 2015.
40
Fundamental Concepts
Convolutional Neural Networks (CNNs) Computing Architecture
43. Why is this important?
The big opportunity: Data Reuse
Ref. Michael Pellauer, et al., “MODELING AND ANALYZING DEEP
LEARNING ACCELERATOR DATAFLOWS WITH MAESTRO,” in MAERI Tutorial, 2018.
41
Fundamental Concepts
Data Movement and its costs in CNN Architectures
44. - Fetch Data
- Multiply and Accumulation
Data Flow
+
Computing
Localization
Data Reuse
Ref. Michael Pellauer, et al., “MODELING AND ANALYZING DEEP
LEARNING ACCELERATOR DATAFLOWS WITH MAESTRO,” in MAERI Tutorial, 2018.
42
Fundamental Concepts
Data Flows, a way for Easy and cheap access to the data
46. Ref. Michael Pellauer, et al., “MODELING AND ANALYZING DEEP
LEARNING ACCELERATOR DATAFLOWS WITH MAESTRO,” in MAERI Tutorial, 2018.
44
Fundamental Concepts
Data Flows | a 7D Computational Problem
47. Input-Stationary (IS) Data Flow
Ref. Joel Emer, et al., “Tutorial on Hardware Accelerators for Deep Neural Networks,” ISCA Tutorial, June 2019.
45
Output-Stationary (OS) Data Flow
Weight-Stationary (WS) Data Flow
Row-Stationary (RS) Data Flow