The document discusses the role of the Internet of Things (IoT) and artificial intelligence (AI) in medical and healthcare systems, covering topics such as IoT history, healthcare applications, and the Internet of Nano Things (IoNT). It provides insights into deep learning fundamentals and the architecture of IoT systems, highlighting contributions from Databiox in sharing biomedical datasets. The content includes references to various studies and datasets pertaining to cancer detection and treatment planning using deep learning approaches.

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.

5. IoT for Healthcare
3
Ref.
Hadi Habibzadeh et al. IEEE Internet of Things Journal, 2019

6. IoT for Healthcare
4

7. IoT for Healthcare
5

8. 6
Internet of Nano Things (IoNT)
Ref.
Kyeonghye Guk et al. MDPI, Nanomaterial, 2019.

9. 7
Internet of Nano Things (IoNT)

10. 8
Internet of Nano Things (IoNT)

11. 9
Internet of Nano Things (IoNT)

12. 10
IoT and Artificial Intelligence (AI)
Ref.
Hadi Habibzadeh et al. IEEE Internet of Things Journal, 2019

13. 11
IoT and Artificial Intelligence (AI)
Ref.
Yazdan Ahmad Qadri et al. IEEE COMST 2020

14. Artificial Intelligence (AI)
12

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

16. Biological Neuron
Ref. Jennifer Walinga, 2014. 14

17. Ref. Jennifer Walinga, 2014.
Biological Neuron
15

18. Biological Neuron
Ref. Prof. Dr. Jurgen Brauer, “Introduction to Deep Learning,” 2018. 16

19. Artificial Neural Networks (ANN)
Ref. Prof. Dr. Jurgen Brauer, “Introduction to Deep Learning,” 2018. 17

20. Artificial Neural Networks (ANN)
18

21. Artificial Neural Networks (ANN)
19

22. Deep Neural Networks
20

23. Deep Neural Networks
21

24. 22

25. Deep Learning: Anywhere!
Ref. Dr. Dara Rahmati, Hardware Accelerator Course Slides, 2020. 23

26. 24

27. 25

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

32. 19

33. Grade I Grade II Grade III
Invasive Ductal Carcinoma Grading Dataset
DataBioX: Training dataset for Supervised Learning
31

34. Invasive Ductal Carcinoma Grading Dataset
DataBioX: Training dataset for Supervised Learning
32

35. Published Paper in 2020
Elsevier Publication
Officially requested by many prestigious
Universities around the world like MIT and
University of Johns Hopkins.
33

36. Breast Cancer Classification Dataset
BreaKHis: Training dataset for Supervised Learning
cmccool@uni-bonn.de
34

37. Breast Cancer Detection
Deep Learning Applied for Histological Diagnosis of Breast Cancer (2020)
35

38. Breast Cancer Detection
Deep Learning to Improve Breast Cancer Detection on Screening Mammography (2019)
36

39. Breast Cancer Treatment Plan
Early prediction of neoadjuvant chemotherapy response for advanced breast cancer
Using PET/MRI image deep learning
37

40. Colon Cancer Analysis
Deep learning for colon cancer histopathological images analysis (2021)
38

41. Glioblastoma Detection
Analyzing MRI scans to detect glioblastoma tumor using hybrid deep belief networks (2020)
39

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

45. Spatial Architecture
Data Flow
Temporal Architecture
SIMD/SIMT
43
Fundamental Concepts
Data Flows | Spatial and Temporal Architetcures

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

48. 46
Fundamental Concepts
NVDLA Deep Learning Accelerator | WS Data Flow

49. 47
Fundamental Concepts
Eyeriss Deep Learning Accelerator | RS Data Flow

50. Input Parameter
No.
LayerFile
1
dataFlowFile
2
vectorWidth
3
NoCBandwidth
4
multicastSupported
5
numAverageHopsinNoC
6
numPEs
7
48
Simulation Tools
MAESTRO

51. 27
Buffer Analysis
Data Flow NLR NVDLA
L1 Buffer Requirement
(Byte)
18.00 66.00
L2 Buffer Requirement
(KB)
1.12 4.12
L1RdSum 7,225,344 451,584
L1WrSum 7,225,344 451,584
L2RdSum 462,422,016 28,901,376
L2WrSum 462,422,016 28,901,376
L1 Weight Reuse 1 16
L1 Input Reuse 4 16
L2 Weight Reuse 448 190.26
L2 Input Reuse 2,633 4,473
NoC Analysis
L1 to L2 NoC BW 128 32
L2 to L1 NoC BW 160 1,024
Performance Analysis
L1 to L2 Sum 56 32
L1 to L2 Delay 4.43 4.25
L2 to L1 Delay 0 0
Roofline Throughput
(GFLOPS with 1 GHZ
clock)
896 128
Compute Runtime 169 421
Total Runtime (Cycles) 1,428,553,728 384,072,192
1,428,553,728
384,072,192
0
200,000,000
400,000,000
600,000,000
800,000,000
1,000,000,000
1,200,000,000
1,400,000,000
1,600,000,000
Total runtime (Cycles)
NLR vgg16_conv11 NVDLA vgg16_conv11
1
4
16 16
0
2
4
6
8
10
12
14
16
18
L1 weight reuse L1 input reuse
NLR vgg16_conv11 NVDLA vgg16_conv11
49
DLA Performance Evaluation

52. Thanks
s.jassbi@srbiau.ac.ir
hamidreza.bolhasani@srbiau.ac.ir