Chips de IA para aplicaciones perimetrales 2024-2034: Inteligencia artificial en la periferia

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:

 

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.

 

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.

 

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.

 

 

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:

 

This report provides critical market intelligence concerning AI hardware at the edge, particularly chips used for accelerating machine learning workloads. This includes:

 

 

 

 

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