M2MEnterprise IoT

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Author: Andrew Brown
Publication Date: Apr 01 2021
Pages: 13
 Report Type: Report, Word

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IoT

The Dawn of Endpoint AI: Bringing Compute Closer to Data (Abridged)



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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)

Arm engaged Strategy Analytics as a partner in the preparation of this report to validate the market for Endpoint AI. 
Table of Contents

Contents
1. Introduction 3
2. From Edge to Endpoint: Why Intelligence is Moving to the Device 3
3. Data is Driving the shift from Centralized to Distributed 3
4. From Edge to Endpoint: AI and Machine Learning Changing the Edge Paradigm 4
5. Endpoint AI: Sensing, Inference and Action 5
6. Endpoint AI: Mighty Oaks from Tiny Acorns Grow 6
7. TinyML, MCUs and Artificial Intelligence 7
8. Endpoint Intelligence is Critical for the Three Vs: Vibration, Voice and Vision 8
9. Use Cases 10
9.1 Predictive Maintenance (PdM) 10
9.2 Consumer and Smart Home 10
9.3 Healthcare 11
10. Conclusions 11
11. Analyst Contacts 12

Table of Exhibits
Figure 1: The Transitions from Centralized to Distributed Computing 4
Figure 2: Global Internet Device Installed Base 5
Figure 3: The Deep Learning Process 6
Figure 4: AI toolsets perform model conversion to run inferences of optimized neural networks on MCUs 7
Figure 5: IoT B2B Application Usage-Current and Future 9
 

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