The revenue of the edge AI hardware market was 1058 million units in 2022, and it will grow at a rate of 20.50% in the years to come, to touch 4704 million units by 2030, as stated by a market research firm P&S Intelligence.
For edge computing, latency is fundamentally non-existent since this approach makes approximately zero separation between the sites where data is produced and processed.
AI processes IoT-generated data to near-end devices with the use of ML algorithms to cope with matters of high latency and low latency. An IoT device gathers loads of data, sent to the cloud where ML models operate and send the handled data back to the device, possibly postponing the response.
Therefore, speed of the network is increased and latency decreased with the use of edge computing. Every msec counts toward data availability, so, microseconds and not milliseconds are used to measure latency.
On-device AI, though, reduces data transfer, allowing a quicker response to the query that is generated.
Moreover, storing lots of data in the cloud may not be a practical option. Since the capacity of processing for edge AI is situated on the device, it is not essential to transmit data to the cloud.
Smart cities make use of a number of systems for supporting the life cycle of people better in qualitative and quantitative ways. These systems are available in many forms, such as, smart energy, smart buildings, smart homes, smart farming, smart manufacturing, and smart healthcare.
With more people choosing to live in cities, there will be greater requirement for automated services in the daily life. Therefore, the requirement for smart homes is changing from luxury to major necessities.
Furthermore, quite a lot of consumer electronics employed in smart homes, such as, wearables, gaming consoles, smart speakers, drones, and home automation robots, use edge AI. The uses of this technology are placed on natural language processing, computer vision, HMI, and customer experience.
Owing to the arrival of 5G in APAC, the increasing count of IoT-integrated devices, and the growing use of smartphones in Japan, India, China, and South Korea, generated the largest revenue in the edge AI hardware market in the recent past.
Another significant factor in the increasing requirement for vision processing unit integration to accelerate AI actions is wearable technology. APAC has a lot of potential for manufacturing, telecom, and automotive industries, generating higher requirement for such devices.
Due to the increasing requirement for smart homes and smart cities, the demand for edge AI hardware will grow like anything in the years to come, all over the world.
