Summary

As AI moves to the edge, edge AI chipsets becomes more important. Edge AI chipsets refers to computational chipsets focusing on AI workload that is typical deployed in edge environments, which include end devices, gateways and on-premise servers. This chipset is generally designed for AI inference workload, though in some cases, they can also support some level of AI training, particularly the training of deep learning models.

Overall, ABI Research estimates that the annual global edge AI chipset revenues for 2018 is US$10.6 billion. The market has experienced strong growth in the past and is expected to continue to grow to US$71 billion by 2024, with a CAGR of 31% between 2019 and 2024. Such strong growth is propelled by migration of AI inference workload to the edge, particularly in the smartphones, smart home, automotive, wearables, and robotics industry.

This report explores the dynamic landscape of edge AI landscape. By looking at chipset architecture, their respective computational requirements and use cases, the report provides a holistic view on the current state and future trends of edge AI chipset. Key players in the edge AI chipset industry have also been profiled with their key capabilities highlighted.

In addition, the report also looks into current development in open-source chipset. Under RISC-V, open-source chipset startups have started to develop AI-dedicated chipset with high parallelistic computing capabilities. Due to participation and contributions from across the industry, open-source AI chipsets will be more in line with market requirements and expectations, significantly reducing the cost of error and development costs in product maintenance and upgrade.

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