Specialized edge AI hardware that enables quick deep learning on-device has become essential due to the rising need for real-time deep learning workloads. Additionally, a cloud-based AI method cannot ensure data privacy, low latency, or offer high bandwidth. As a result, many AI workloads are shifting to the edge, increasing the demand for specialized AI hardware for on-device machine learning inference.

The growth of IoT, smart technology adoption by consumer electronics and the automotive industry, and intelligent industrial automation are propelling the edge AI accelerator market. AI accelerators in consumer-oriented applications, such as smartphones, wearables, and smart appliances, need to have a high processing-to-cost ratio as well as a smaller size. On the other hand, for most of the AI accelerators used in industrial/enterprise applications, the requirement for high processing speed and power efficiency are of prime significance.

The majority of chip manufacturers are struggling to improve processing speed while reducing power consumption. To overcome this, organizations are investing in developing application-specific chips, efficient chip architectures, new algorithms, advanced memories, and alternative materials. To leverage these technological advancements, major corporations are embracing technology strategies such as partnerships and acquisitions.

The market for edge AI accelerators is projected to grow significantly in the United States, South Korea, China, Japan, Germany, and Israel. This is due to the high amount of manufacturing activity pertaining to consumer electronics, automotive, industrial equipment, and defense. Apart from having a strong manufacturing base, these countries have also developed a strong ecosystem for chip manufacturing, which is crucial to maintaining a dominant position in the market.

The emergence of deep learning, neural networks, computer vision, generative artificial intelligence, and neuromorphic computing has created new opportunities for edge inferencing applications. While enterprises are quickly moving towards a decentralized computer architecture, they are also learning new methods to apply this technology to boost productivity and cut costs. Therefore, AI chip developers should focus more on developing solutions that are designed to fulfill these requirements specific to use cases.

This Frost & Sullivan research report covers the following topics:

•     Overview and significance of key AI accelerator technologies
•     Comparative analysis of key edge AI processors
•     Emerging use cases
•     Technology trends and key developmental strategies used by players in the industry
•     Business models in the AI accelerator chip industry
•     Regional analysis of the edge AI accelerator space
•     AI accelerators roadmap
•     Growth opportunities