Here's a sample of the report "AI Chips for Edge Applications 2024-2034". The full report costs about $11,000. What a bargain! Who wants to "chip" in for a copy?
BTW, we are listed in the Hardware Start-Up and New Players diagram.
Annual revenue generated by AI Chips for edge devices is set to exceed US$22 billion by 2034.
AI Chips for Edge Applications 2024-2034: Artificial Intelligence at the Edge
Technology analyses and market forecasts for the global sale of AI chips for edge applications by geography, architecture, packaging, end-user, application, and industry vertical.
The global AI chips market for edge devices will grow to US$22.0 billion by 2034, with the three largest industry verticals at that time being Consumer Electronics, Industrial, and Automotive. Artificial Intelligence (AI) is already displaying significant transformative potential across a number of different applications, from fraud detection in high-frequency trading to the use of generative AI (such as the likes of ChatGPT) as a significant time-saver for the preparation of written documentation, as well as a creative prompt. While the use of semiconductor chips with neural network architectures (these architectures being especially well-equipped in handling machine learning workloads, machine learning being an integral facet to functioning AI) is prevalent within data centers, it is at the edge where significant opportunity for adoption of AI lies. The benefits to end-users of providing a greater array of functionalities to edge devices, as well as - in certain applications - being able to fully outsource human-hours to intelligent systems, is significant. AI has already found its way into the flagship smartphones of the world's leading designers, and is set to be rolled out across a number of different devices, from automotive vehicles to smart appliances in the home.
Following a period of dedicated research by expert analysts, IDTechEx has published a report that offers unique insights into the global edge AI chip technology landscape and corresponding markets. The report contains a comprehensive analysis of 23 players involved with AI chip design for edge devices, as well as a detailed assessment of technology innovations and market dynamics. The market analysis and forecasts focus on total revenue (where this corresponds to the revenue that can be attributed to the specific neural network architecture included in sold chips/chipsets that is responsible for handling machine learning workloads), with granular forecasts that are segmented by geography (APAC, Europe, North America, and Rest of World), type of buyer (consumer and enterprise), chip architecture (GPU, CPU, ASIC, DSP, and FPGA), packaging type (System-on-Chip, Multi-Chip Module, and 2.5D+), application (language, computer vision, and predictive), and industry vertical (industrial, healthcare, automotive, retail, media & advertising, consumer electronics, and others).
The report presents an unbiased analysis of primary data gathered via our interviews with key players, and it builds on our expertise in the semiconductor, computing and electronics sectors.
This research delivers valuable insights for:
- Companies that require AI-capable hardware.
- Companies that design/manufacture AI chips and/or AI-capable embedded systems.
- Companies that supply components used in AI-capable embedded systems.
- Companies that invest in AI and/or semiconductor design, manufacture, and packaging.
- Companies that develop devices that may require AI functionality.
Computing can be segmented with regards to the different environments, designated by where computation takes place within the network (i.e. within the cloud or at the edge of the network). This report covers the consumer edge and enterprise edge environments. Source: IDTechEx
Artificial Intelligence at the Edge
The differentiation between edge and cloud computing environments is not a trivial one, as each environment has its own requirements and capabilities. An edge computing environment is one in which computations are performed on a device - usually the same device on which the data is created - that is at the edge of the network (and, therefore, close to the user). This contrasts with cloud or data center computing, which is at the center of the network. Such edge devices include cars, cameras, laptops, mobile phones, autonomous vehicles, etc. In all of these instances, computation is carried out close to the user, at the edge of the network where the data is located. Given this definition of edge computing, edge AI is therefore the deployment of AI applications at the edge of the network, in the types of devices listed above. The benefits of running AI applications on edge devices include not having to send data back and forth between the cloud and the edge device to carry out the computation; as such, edge devices running AI algorithms can make decisions quickly without needing a connection to the internet or the cloud. Given that many edge devices run on a power cell, AI chips used for such edge devices need to have lower power consumption than within data centers, in order to be able to run effectively on these devices. This results in typically simpler algorithms being deployed, that don't require as much power.
Edge devices can be split into two categories depending on who they are intended for; consumer devices are sold directly to end-users, and so are developed with end-user requirements in mind. Enterprise devices, on the other hand, are purchased by businesses or institutions, who may have different requirements to the end-user. Both types of edge devices are considered in the report.
The consumer electronics, industrial, and automotive industry verticals are expected to generate the most revenue for AI chips at the edge by 2034. Source: IDTechEx
AI: A crucial technology for an Internet of Things
AI's capabilities in natural language processing (understanding of textual data, not just from a linguistic perspective but also a contextual one), speech recognition (being able to decipher a spoken language and convert it to text in the same language, or convert to another language), recommendation (being able to send personalized adverts/suggestions to consumers based on their interactions with service items), reinforcement learning (being able to make predictions based on observations/exploration, such as is used when training agents to play a game), object detection, and image classification (being able to distinguish objects from an environment, and decide on what that object is) are such that AI can be applied to a number of different devices across industry verticals and thoroughly transform the ways in which human users interact with these devices. This can range from additional functionality that enhances user experience (such as in smartphones, smart televisions, personal computers, and tablets), to functionality that is inherently crucial to the technology (such as is the case for autonomous vehicles and industrial robots, which would simply not be able to function in the desired manner without the inclusion of AI).
The Smart Home in particular is a growing avenue for AI (which primarily comprises consumer electronics products), given that artificial intelligence (allowing for automation and hands-free access) and Wi-Fi connectivity are two key technologies for realizing an Internet of Things (IoT), where appliances can communicate directly with one another. Smart televisions, mirrors, virtual reality headsets, sensors, kitchen appliances, cleaning appliances, and safety systems are all devices that can be brought into a state of interconnectivity through the deployment of artificial intelligence and Wi-Fi, where AI allows for hands-free access and voice command over smart home devices. The opportunity afforded by bringing AI into the home is reflected somewhat by the growth of the consumer electronics vertical over the forecast period, with it being the industry that generates the most revenue for edge AI chips in 2034.
The Edge AI chip landscape. Source: IDTechEx
The growth of AI at the edge
While the forecast presented in this report does predict substantial growth of AI at the edge over the next ten years - where global revenue is in excess of US$22 billion by 2034 - this growth is anything but steady. This is due to the saturation and stop-start nature of certain markets that have already employed AI architectures in their incumbent chipsets, and where rigorous testing is necessary prior to high volume rollout, respectively. For example, the smartphone market has already begun to saturate; though premiumization of smartphones continues (where the percentage share of total smartphones sold given over to premium smartphones is, year-on-year, increasing), where AI revenue increases as more premium smartphones are sold given that these smartphones incorporate AI coprocessing in their chipsets, it is expected that this will itself begin to saturate over the next ten years.
In contrast to this, two notable jumps in revenue on the forecast presented in the report are from 2024 to 2025, and 2026 to 2027. The first of these jumps can be largely attributed to the most cutting-edge ADAS (Advanced Driver-Assistance Systems) finding their way into car manufacturers' 2025 production line. The second jump is due in part to increased adoption of ADAS systems, as well as the relative maturation of start-ups operating presently targeting embedded devices, especially for smart home appliances. These applications are discussed in greater detail in the report, with a particular focus on the smartphone and automotive markets.
Smartphone price as compared to the node process that incumbent chipsets have been manufactured in. This plot has been created from a survey - carried out specifically for this report - of 196 smartphones released since 2020, 91 of which incorporate neural network architectures to allow for AI acceleration. Source: IDTechEx
Market developments and roadmaps
IDTechEx's model of the edge AI chips market considers architectural trends, developments in packaging, the dispersion/concentration of funding and investments, historical financial data, individual industry vertical market saturation, and geographically-localized ecosystems to give an accurate representation of the evolving market value over the next ten years.
Our report answers important questions such as:
- Which industry verticals will AI chips for edge devices be used most prominently in?
- What opportunities are there for growth within the edge computing environments?
- How has the adoption of AI within more mature markets been received, and what are the obstacles to adoption in more emergent applications?
- How will each AI chip application and industry vertical grow in the short and long-term?
- What are the trends associated with the design and manufacture of chips that incorporate neural network architectures?
Summary
This report provides critical market intelligence concerning AI hardware at the edge, particularly chips used for accelerating machine learning workloads. This includes:
Market forecasts and analysis
- Market forecasts from 2024-2034, segmented in six different ways: by geography, architecture, packaging, end-user, application and industry vertical.
- Analysis of market forecasts, including assumptions, methodologies, limitations, and explanations for the characteristics of each forecast.
A review of the technology behind AI chips
- History and context for AI chip design and manufacture.
- Overview of different architectures.
- General capabilities of AI chips.
- Review of semiconductor manufacture processes, from raw material to wafer to chip.
- Review of the physics behind transistor technology.
- Review of transistor technology development, and industry/company roadmaps in this area.
- Analysis of the benchmarking used in the industry for AI chips.
Surveys and analysis of key edge AI applications
- Analysis of the chipsets included in almost 200 smartphones released since 2020, along with pricing estimations and key trends.
- Analysis of the chipsets included in almost 50 tablets released since 2020, along with pricing estimations and key trends.
- Performance comparisons for automotive chipsets, along with key trends with regards performance, power consumption, and efficiency.
Full market characterization for each major edge AI chip product
- Review of the edge AI chip landscape, including key players across edge applications.
- Profiles of 23 of the most prominent companies designing AI chips for edge applications today, with a focus on their latest and in-development chip technologies.
- Reviews of promising start-up companies developing AI chips for edge applications.
| Report Metrics | Details |
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| Historic Data | 2019 - 2022 |
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| CAGR | The global market for AI chips at the edge will reach US$22.0 billion by 2034. This represents a CAGR of 7.63% over the forecast period (2024 to 2034). |
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| Forecast Period | 2024 - 2034 |
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| Forecast Units | USD$ Billions |
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| Regions Covered | Worldwide, All Asia-Pacific, North America (USA + Canada), Europe |
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| Segments Covered | Geography (North America, APAC, Europe, Rest of World), architecture (FPGA, CPU, GPU, DSP, ASIC), packaging (SoC, MCM, 2.5D+), end-user (consumer, enterprise), application (computer vision, language, predictive), and industry vertical (consumer electronics, industrial, automotive, healthcare, retail, media & advertising, other). |
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Analyst access from IDTechEx
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Table of Contents
| 1. | EXECUTIVE SUMMARY |
| 1.1. | Edge AI |
| 1.2. | IDTechEx definition of Edge AI |
| 1.3. | Edge vs Cloud characteristics |
| 1.4. | Advantages and disadvantages of edge AI |
| 1.5. | Edge devices that employ AI chips |
| 1.6. | The edge AI chip landscape - overview |
| 1.7. | The edge AI chip landscape - key hardware players |
| 1.8. | The edge AI chip landscape - hardware start-ups |
| 1.9. | The AI chip landscape - other than hardware |
| 1.10. | Edge AI landscape - geographic split: China |
| 1.11. | Edge AI landscape - geographic split: North America |
| 1.12. | Edge AI landscape - geographic split: Rest of World |
| 1.13. | Inference at the edge |
| 1.14. | Deep learning: How an AI algorithm is implemented |
| 1.15. | AI chip capabilities |
| 2. | FORECASTS |
| 2.1. | Total revenue forecast |
| 2.2. | Methodology and analysis |
| 2.3. | Estimating annual revenue from smartphone chipsets |
| 2.4. | Smartphone chipset costs |
| 2.5. | Costs garnered by AI in smartphone chipsets |
| 2.6. | Revenue forecast by geography |
| 2.7. | Percentage shares of market by geography |
| 2.8. | Chip types: architecture |
| 2.9. | Forecast by chip type |
| 2.10. | Semiconductor packaging timeline |
| 2.11. | From 1D to 3D semiconductor packaging |
| 2.12. | 2D packaging - System-on-Chip |
| 2.13. | 2D packaging - Multi-Chip Modules |
| 2.14. | 2.5D and 3D packaging - System-in-Package |
| 2.15. | 3D packaging - System-on-Package |
| 2.16. | Forecast by packaging |
| 2.17. | Consumer vs Enterprise forecast |
| 2.18. | Forecast by application |
| 2.19. | Forecast by industry vertical |
| 2.20. | Forecast by industry vertical - full |
| 3. | TECHNOLOGY: FROM SEMICONDUCTOR WAFERS TO AI CHIPS |
| 3.1. | Wafer and chip manufacture processes |
| 3.1.1. | Raw material to wafer: process flow |
| 3.1.2. | Wafer to chip: process flow |
| 3.1.3. | Wafer to chip: process flow |
| 3.1.4. | The initial deposition stage |
| 3.1.5. | Thermal oxidation |
| 3.1.6. | Oxidation by vapor deposition |
| 3.1.7. | Photoresist coating |
| 3.1.8. | How a photoresist coating is applied |
| 3.1.9. | Lithography |
| 3.1.10. | Lithography: DUV |
| 3.1.11. | Lithography: Enabling higher resolution |
| 3.1.12. | Lithography: EUV |
| 3.1.13. | Etching |
| 3.1.14. | Deposition and ion implantation |
| 3.1.15. | Deposition of thin films |
| 3.1.16. | Silicon Vapor Phase Epitaxy |
| 3.1.17. | Atmospheric Pressure CVD |
| 3.1.18. | Low Pressure CVD and Plasma-Enhanced CVD |
| 3.1.19. | Atomic Layer Deposition |
| 3.1.20. | Molecular Beam Epitaxy |
| 3.1.21. | Evaporation and Sputtering |
| 3.1.22. | Ion Implantation: Generation |
| 3.1.23. | Ion Implantation: Penetration |
| 3.1.24. | Metallization |
| 3.1.25. | Wafer: The final form |
| 3.1.26. | Semiconductor supply chain players |
| 3.2. | Transistor technology |
| 3.2.1. | How transistors operate: p-n junctions |
| 3.2.2. | How transistors operate: electron shells |
| 3.2.3. | How transistors operate: valence electrons |
| 3.2.4. | How transistors work: back to p-n junctions |
| 3.2.5. | How transistors work: connecting a battery |
| 3.2.6. | How transistors work: PNP operation |
| 3.2.7. | How transistors work: PNP |
| 3.2.8. | How transistors switch |
| 3.2.9. | From p-n junctions to FETs |
| 3.2.10. | How FETs work |
| 3.2.11. | Moore's law |
| 3.2.12. | Gate length reductions |
| 3.2.13. | FinFET |
| 3.2.14. | GAAFET, MBCFET, RibbonFET |
| 3.2.15. | Process nodes |
| 3.2.16. | Device architecture roadmap |
| 3.2.17. | Evolution of transistor device architectures |
| 3.2.18. | Carbon nanotubes for transistors |
| 3.2.19. | CNTFET designs |
| 3.2.20. | Semiconductor foundry node roadmap |
| 3.2.21. | Roadmap for advanced nodes |
| 4. | EDGE INFERENCE AND KEY APPLICATIONS |
| 4.1. | Inference at the edge and benchmarking |
| 4.1.1. | Edge AI |
| 4.1.2. | Edge vs Cloud characteristics |
| 4.1.3. | Advantages and disadvantages of edge AI |
| 4.1.4. | Edge devices that employ AI chips |
| 4.1.5. | AI in smartphones and tablets |
| 4.1.6. | Recent history: Siri |
| 4.1.7. | Text-to-speech |
| 4.1.8. | AI in personal computers |
| 4.1.9. | AI chip basics |
| 4.1.10. | Parallel computing |
| 4.1.11. | Low-precision computing |
| 4.1.12. | AI in speakers |
| 4.1.13. | AI in smart appliances |
| 4.1.14. | AI in automotive vehicles |
| 4.1.15. | AI in sensors and structural health monitoring |
| 4.1.16. | AI in security cameras |
| 4.1.17. | AI in robotics |
| 4.1.18. | AI in wearables and hearables |
| 4.1.19. | The edge AI chip landscap |
| 4.1.20. | Inference at the edge |
| 4.1.21. | Deep learning: How an AI algorithm is implemented |
| 4.1.22. | AI chip capabilities |
| 4.1.23. | AI chip capabilities |
| 4.1.24. | MLPerf - Inference |
| 4.1.25. | MLPerf Edge |
| 4.1.26. | Inference: Edge, Nvidia vs Nvidia |
| 4.1.27. | MLPerf Mobile - Qualcomm HTP |
| 4.1.28. | The battle for domination: Qualcomm vs MediaTek |
| 4.1.29. | MLPerf Tiny |
| 4.2. | AI in smartphones |
| 4.2.1. | Mobile device competitive landscape |
| 4.2.2. | Samsung and Oppo chipsets |
| 4.2.3. | US restrictions on China |
| 4.2.4. | Smartphone chipset landscape 2022 - Present |
| 4.2.5. | MediaTek and Qualcomm 2020 - Present |
| 4.2.6. | AI processing in smartphones: 2020 - Present |
| 4.2.7. | Node concentrations 2020 - Present |
| 4.2.8. | Chipset concentrations 2020 - Present |
| 4.2.9. | Chipset designer concentrations 2020 - Present |
| 4.2.10. | Node concentrations for each chipset designer |
| 4.2.11. | AI-capable versus non AI-capable smartphones |
| 4.2.12. | Chipset volume: 2021 and 2022 |
| 4.3. | AI in tablets |
| 4.3.1. | Tablet competitive landscape |
| 4.3.2. | Tablet chipset landscape 2020 - Present |
| 4.3.3. | AI processing in tablets: 2020 - Present |
| 4.3.4. | Node concentrations 2020 - Present |
| 4.3.5. | Chipset designer concentrations 2021 - Present |
| 4.3.6. | Node concentrations for each chipset designer |
| 4.3.7. | AI-capable versus non AI-capable tablets |
| 4.4. | AI in automotive |
| 4.4.1. | AI in automobiles: Competitive landscape |
| 4.4.2. | Levels of driving automation |
| 4.4.3. | Computational efficiencies |
| 4.4.4. | AI chips for automotive vehicles |
| 4.4.5. | Performance and node trends |
| 4.4.6. | Rising power consumption |
| 5. | SUPPLY CHAIN PLAYERS |
| 5.1. | Smartphone chipset case studies |
| 5.1.1. | MediaTek: Dimensity and APU |
| 5.1.2. | Qualcomm: MLPerf results - Inference Mobile and Inference Tiny |
| 5.1.3. | Qualcomm: Mobile AI |
| 5.1.4. | Apple: Neural Engine |
| 5.1.5. | Apple: The ANE's capabilities and shortcomings |
| 5.1.6. | Google: Pixel Neural Core and Pixel Tensor |
| 5.1.7. | Google: Edge TPU |
| 5.1.8. | Samsung: Exynos |
| 5.1.9. | Huawei: Kirin chipsets |
| 5.1.10. | Unisoc: T618 and T710 |
| 5.2. | Automotive case studies |
| 5.2.1. | Nvidia: DRIVE AGX Orin and Thor |
| 5.2.2. | Qualcomm: Snapdragon Ride Flex |
| 5.2.3. | Ambarella: CV3-AD685 for automotive applications |
| 5.2.4. | Ambarella: CVflow architecture |
| 5.2.5. | Hailo |
| 5.2.6. | Blaize |
| 5.2.7. | Tesla: FSD |
| 5.2.8. | Horizon Robotics: Journey 5 |
| 5.2.9. | Horizon Robotics: Journey 5 Architecture |
| 5.2.10. | Renesas: R-Car 4VH |
| 5.2.11. | Mobileye |
| 5.2.12. | Mobileye: EyeQ Ultra |
| 5.2.13. | Texas Instruments: TDA4VM |
| 5.3. | Embedded device case studies |
| 5.3.1. | Nvidia: Jetson AGX Orin |
| 5.3.2. | NXP Semiconductors: Introduction |
| 5.3.3. | NXP Semiconductors: MCX N |
| 5.3.4. | NXP Semiconductors: i.MX 95 and NPU |
| 5.3.5. | Intel: AI hardware portfolio |
| 5.3.6. | Intel: Core |
| 5.3.7. | Perceive |
| 5.3.8. | Perceive: Ergo 2 architecture |
| 5.3.9. | GreenWaves Technologies |
| 5.3.10. | GreenWaves Technologies: GAP9 architecture |
| 5.3.11. | AMD Xilinx: ACAP |
| 5.3.12. | AMD: Versal AI |
| 5.3.13. | NationalChip: GX series |
| 5.3.14. | NationalChip: GX8002 and gxNPU |
| 5.3.15. | Efinix: Quantum architecture |
| 5.3.16. | Efinix: Titanium and Trion FPGAs |
| 6. | APPENDICES |
| 6.1. | List of smartphones surveyed |
| 6.1.1. | Appendix: List of smartphones surveyed - Apple and Asus |
| 6.1.2. | Appendix: List of smartphones surveyed - Google and Honor |
| 6.1.3. | Appendix: List of smartphones surveyed - Huawei, HTC and Motorola |
| 6.1.4. | Appendix: List of smartphones surveyed - Nokia, OnePlus, Oppo |
| 6.1.5. | Appendix: List of smartphones surveyed - realme |
| 6.1.6. | Appendix: List of smartphones surveyed - Samsung and Sony |
| 6.1.7. | Appendix: List of smartphones surveyed - Tecno Mobile |
| 6.1.8. | Appendix: List of smartphones surveyed - Xiaomi |
| 6.1.9. | Appendix: List of smartphones surveyed - Vivo and ZTE |
| 6.2. | List of tablets surveyed |
| 6.2.1. | Appendix: List of tablets surveyed - Acer, Amazon and Apple |
| 6.2.2. | Appendix: List of tablets surveyed - Barnes & Noble, Google, Huawei, Lenovo |
| 6.2.3. | Appendix: List of tablets surveyed - Microsoft, OnePlus, Samsung, Xiaomi |
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AI Chips for Edge Applications 2024-2034: Artificial Intelligence at the Edge
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This report characterizes the edge AI chip markets, technologies, and players. Granular forecasts over a 10-year period (up to and including 2034) across 6 different areas (therein 3 primary regional geographies, consumer and enterprise use, 5 device architectures (GPUs, CPUs, ASICs, DSPs and...
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Unsupervised segmention of moving objects
Discussion 
possibly be a candidate for the mysterious Custom Customer SoC, featured in the recent Investor Roadshow presentation (provided the licensing of Akida IP was done via MegaChips)? 
