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Author: Andrew Brown
Publication Date: 3月 30 2021
Pages: 23
Report Type: Report, Word

The Dawn of Endpoint AI: Bringing Compute Closer to Data

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Report Summary:

Major advances in neural networks for embedded devices, such as microcontrollers, are helping to drive new capabilities into endpoint devices themselves, driven by needs for real-time information, automation and in most cases, near-instant responses.

This report presents the context and opportunity for the development of Endpoint AI (Artificial Intelligence)

Table of Contents

1. From Edge to Endpoint: Why Intelligence is Moving to the Device 4
2. Data is Driving the shift from Centralized to Distributed 5
3. From Edge to Endpoint: AI and Machine Learning Changing the Edge Paradigm 6
4. Endpoint AI: Sensing, Inference and Action 7
4.1 Sensing 8
4.2 Inferring 8
4.3 Acting 9
5. Endpoint AI: Mighty Oaks from Tiny Acorns Grow 10
6. TinyML, MCUs and Artificial Intelligence 11
7. Endpoint Intelligence is Critical for the Three Vs: Vibration, Voice and Vision 13
7.1 Vibration 15
7.2 Voice 16
7.3 Vision 17
8. Use Cases 17
8.1 Predictive Maintenance (PdM) 17
8.1.1 Vibrational analysis 17
8.1.2 Magnetic Sensor Fusion 18
8.1.3 Acoustical analysis (sonic) 18
8.1.4 Acoustical analysis (ultrasonic) 18
8.1.5 Thermal Imaging 18
8.2 Consumer and Smart Home 19
8.3 Healthcare 20
9. Conclusions 21
10. Analyst Contacts 22

Table of Exhibits
Figure 1: The Transitions from Centralized to Distributed Computing 5
Figure 2: Global Internet Device Installed Base 6
Figure 3: The Deep Learning Process 7
Figure 4: Vision, Vibration and Voice in MCUs 9
Figure 5: AI toolsets perform model conversion to run inferences of optimized neural networks on MCUs 12
Figure 6: IoT B2B Application Usage-Current and Future 14
Figure 7: Sensor Fusion Enabling context awareness 15

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