The On-device AI Market for IoT Applications

Description

List of Tables

Berg Insight estimates that the revenues generated by on-device AI solutions reached US$ 10.1 billion in 2024, an increase of 22 percent year-on-year. This figure includes AI SoCs/SoMs, AI accelerators, AI MCUs and specialised on-device AI software and platforms, but excludes revenues generated by non-IoT applications such as smartphones, tablets and personal computers. The market is expected to grow to US$ 30.6 billion in 2029, representing a CAGR of 25 percent. Get up to date with the latest trends and developments with this unique 90-page report.

Highlights from the report:

Insights from numerous executive interviews with market leading companies.
360-degree overview of the on-device AI ecosystem.
Market value forecast for on-device AI hardware and software until 2029.
Market shares for 40 key on-device AI hardware and software providers.
Detailed profiles of 31 key on-device AI hardware and software providers.
Use case descriptions across the most important industry verticals.
In-depth analysis of market trends and key developments.

List of Figures

Executive Summary

1. Introduction

1.1. Cloud vs on-device processing
- 1.1.1. On-device processing
- 1.1.2. Cloud processing
- 1.1.3. Edge data centre processing
- 1.1.4. Hybrid approaches
1.2. AIoT: The convergence of AI and IoT
- 1.2.1. What constitutes an IoT device?
- 1.2.2. IoT connectivity options
1.3. Artificial intelligence technology overview
- 1.3.1. Artificial intelligence
- 1.3.2. Machine learning
- 1.3.3. Deep learning
- 1.3.4. Generative AI
1.4. On-device AI ecosystem
- 1.4.1. On-device AI hardware
- 1.4.2. On-device AI software
- 1.4.3. On-device AI models
- 1.4.4. On-device AI platforms

2. Market Analysis

2.1. The on-device AI industry landscape
- 2.1.1. AI SoC/SoM providers
- 2.1.2. AI accelerator providers
- 2.1.3. AI MCU providers
- 2.1.4. On-device AI platform providers
2.2. Market sizing and forecast
- 2.2.1. Automotive on-device AI market size
- 2.2.2. IoT on-device AI market size
- 2.2.3. On-device GenAI vs non-GenAI market size
- 2.2.4. On-device AI processor shipments
2.3. Solution provider market shares
2.4. Vertical adoption and use cases
- 2.4.1. On-device AI in automotive IoT applications
- 2.4.2. On-device AI in industrial IoT applications
- 2.4.3. On-device AI in wearables
- 2.4.4. On-device AI in retail IoT applications
- 2.4.5. On-device AI in buildings & security IoT applications
- 2.4.6. On-device AI in smart home applications
- 2.4.7. On-device AI in other IoT applications

3. Company Profiles and Strategies

3.1. Ambarella
3.2. Ambiq
3.3. Advanced Micro Devices (AMD)
3.4. Apple
3.5. Axelera
3.6. Black Sesame Technologies
3.7. DEEPX
3.8. EdgeCortix
3.9. Edge Impulse
3.10. EmbedUR
3.11. Hailo
3.12. Horizon Robotics
3.13. Hugging Face
3.14. Intel
3.15. MediaTek
3.16. MemryX
3.17. Mobileye
3.18. Mythic
3.19. Nota AI
3.20. NVIDIA
3.21. NXP Semiconductors
3.22. Qualcomm
3.23. Renesas Electronics
3.24. Rockchip
3.25. SigmaStar
3.26. SiMa
3.27. STMicroelectronics
3.28. Synaptics
3.29. Syntiant
3.30. Tesla
3.31. Texas Instruments
List of Acronyms and Abbreviations

List of Figures

Figure 1.1: Comparison of on-device, edge data centre and cloud processing
Figure 1.2: Comparison between wireless technologies
Figure 1.3: The relationship between AI terminologies
Figure 1.4: Neural network illustration
Figure 1.5: Examples of an AI SoC (left) and SoM (right)
Figure 1.6: Typical deployment methodology
Figure 2.1: Core business activities of key on-device AI vendors
Figure 2.2: On-device AI revenues for IoT applications (World 2023-2029)
Figure 2.3: On-device AI revenues for IoT applications by type (2024 vs 2029)
Figure 2.4: On-device AI processor shipments (World 2023-2029)
Figure 2.5: On-device AI market shares (World 2024)
Figure 2.6: On-device AI revenues by vertical (World 2024)
Figure 2.7: Sales of passenger cars by SAE autonomy level (World 2024-2030)
Figure 2.8: On-device AI enabling autonomous retail at Amazon Go
Figure 3.1: Example of Ambiq's Apollo SoC integration in a smartwatch
Figure 3.2: AMD revenues by segment (2023-2024)
Figure 3.3: Apple's annual revenues by product category (2023-2025)
Figure 3.4: DEEPX's DX-M1 M.2 module
Figure 3.5: EdgeCortix's software stack
Figure 3.6: Edge Impulse workflow example
Figure 3.7: Hailo's stack and deployment methodology
Figure 3.8: Intel revenues by segment (2022-2024)
Figure 3.9: Intel Core Ultra 200U and 200H processors
Figure 3.10: Overview of Mobileye ADAS and autonomous vehicle platforms (Q1-2025)
Figure 3.11: Mythic's AI deployment workflow
Figure 3.12: NVIDIA hardware portfolio for on-device AI processing
Figure 3.13: Nvidia Jetson platform software stack
Figure 3.14: NVIDIA's Automotive & Robotics revenues
Figure 3.15: Qualcomm's Snapdragon Ride AD solutions (April 2025)
Figure 3.16: QCT revenues by segment (2023-2025)
Figure 3.17: Renesas AI vision processor portfolio (March 2025)
Figure 3.18: SigmaStar AI display SoCs
Figure 3.19: SiMa MLSoC Modalix specifications
Figure 3.20: STMicroelectronics' STM32N6 portfolio
Figure 3.21: Syntiant's 1.4mm x 1.8mm NDP100 processor
Figure 3.22: Tesla Hardware 3 module
Figure 3.23: Texas Instruments' edge AI portfolio

The On-device AI Market for IoT Applications - 1st Edition

Description

Table of Contents

List of Tables

Highlights from the report:

Table of Contents

Table of Contents

List of Figures

Executive Summary

1. Introduction

2. Market Analysis

3. Company Profiles and Strategies

List of Figures