Artificial intelligence is rapidly migrating from centralized and cloudy infrastructure to devices that operate at the edge of the network. These devices include smartphones, industrial sensors, smart cameras, autonomous vehicles, and IoT gateways, which process data at the edge of the network and not in the cloud. This migration of artificial intelligence to the edge of the network, or edge computing, is therefore fueling the AI chips market.
Edge computing devices process information at the source of the information and not at the other end of the network. These devices do not require the cloud to make decisions and process information because they process information at the source and send the analyzed information to the cloud.
Explosion of Connected Devices
One of the major drivers of edge AI chips is the increasing rate of connected devices across the globe. Analysts predict that there would be more than 29 billion connected devices deployed globally across industries and applications by 2030.
In addition to traditional IoT devices, there are other categories of AI-enabled devices growing rapidly across the globe. Some of these devices are:
Smart cameras with embedded AI are expected to reach 200 million shipments annually by 2025.
AI-enabled PCs are expected to ship 50 million units annually.
The number of 5G base stations acting as edge AI nodes could exceed 2 million units by 2025.
These devices need chips that can support AI inferencing, leading to increasing demand for edge AI chips.
(Source: Wifitalents)
Why Specialized AI Chips are Essential
In general terms, the development of CPUs and GPUs has traditionally been done with the primary objective of supporting general-purpose computing and cloud computing. However, there are different constraints that affect edge computing, including low power supply, low memory capacity, and the requirement to support real-time processing.
The development of specialized AI chips can mitigate the challenges that affect edge computing through the inclusion of specialized neural processing units, memory systems, and acceleration of ML algorithms to support inference operations with low power requirements.
In addition to the mitigation of challenges, there are performance advantages that can be offered by edge AI systems. For instance, edge AI systems can minimize latency that is experienced in cloud computing systems, which is about 100 milliseconds, to less than 10 milliseconds in edge computing systems. In another instance, edge AI systems can minimize data transmission costs by up to 80 percent compared to cloud computing systems.
(Source: Wifitalents)
Expanding Use Cases Across Industries
The demand for edge AI chips is rising in different industries as organizations are adopting intelligent automation and real-time analytics.
The automotive sector is also using edge AI chips in self-driving cars, which are using edge AI chips to analyze images from cameras and sensors in real-time and make safe decisions while driving. Smart manufacturing is also using edge AI chips to analyze equipment performance and detect anomalies in equipment performance.
The healthcare industry is also witnessing a high adoption rate of edge AI chips, in which medical devices are analyzing the information of patients and providing better privacy and diagnosis.
Conclusion
Edge devices are revolutionizing the artificial intelligence market in a fundamental way. The emergence of billions of connected devices is creating vast volumes of data in real time, and organizations are seeking faster and efficient processing of such information at the edge of the network. The specialized AI chips are facilitating this revolution by providing efficient performance and processing requirements of edge computing. Thus, as industries are adopting AI-based automation, the AI chips market will witness a rapid growth in terms of edge computing-based semiconductors.
FAQs
- Why are edge devices increasing the demand for AI chips?
- Ans: The demand for AI chips is increasing due to the processing and decision-making requirements of edge devices, which require real-time processing and decision making.
- What types of devices use edge AI chips?
- Ans: The devices that make use of edge AI chips include smartphones, autonomous vehicles, industrial sensors, smart cameras, drones, and consumer electronic devices.
- How do edge AI chips improve performance compared to cloud processing?
- Ans: The edge AI chips improve the performance of the devices as they can process the data directly, which helps to reduce latency and bandwidth usage.
- Which industries are adopting edge AI chips the fastest?
- Ans: The industries that are making the fastest adoption of edge AI chips include the automobile industry, manufacturing industry, healthcare industry, retail industry, telecommunication industry, and smart city infrastructure.
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