Edge AI chip market will reach $7.6 billion in 2024, with fierce competition

Diversity is the name of the game when it comes to the edge artificial intelligence ( AI ) chipset industry. In 2019, the AI ​​industry witnessed the continued migration of AI workloads, especially AI inference, to edge devices, including premise servers, gateways, end devices, and sensors . Based on the development of AI in 17 vertical markets, ABI Research expects the edge AI chipset market to grow from $2.6 billion in 2019 to $7.6 billion in 2024, with no single vendor accounting for more than 40% of the market share.

The leader in this market is NVIDIA, with 39% of revenue in the first half of 2019. GPU vendors have a strong presence in key AI verticals and are currently leading in AI deployments, such as automotive, camera systems, robotics, and smart manufacturing . "NVIDIA chooses to release GPU chipsets with different computing and power budgets for different use cases," said Lian Jye Su, principal analyst at ABI Research. "With its large developer ecosystem and partnerships with academic and research institutions, the chipset vendor has formed a strong foothold in the edge AI industry."

NVIDIA is facing stiff competition from Intel , which has a comprehensive chipset portfolio, from Xeon  CPUs to Mobileye and Movidius Myriad. Meanwhile, FPGA vendors such as Xilinx, QuickLogic, and Lattice Semiconductor are creating compelling solutions for industrial AI applications. One vertical missing from NVIDIA’s broad footprint is consumer electronics, specifically smartphones. In recent years, AI processing in smartphones has been driven by smartphone chipset manufacturers and smartphone vendors such as Qualcomm , Huawei , and Apple. In smart home applications, MTK and AmLogic are making their presence known through widespread adoption of voice-controlled front ends and smart devices.

Looking ahead, AI chipset vendors are likely to adopt one of three strategies. The first is to create AI chipsets that target the on-premise server and gateway markets that support AI. These servers and gateways support enterprise use cases, typically with high processing power, and require a flexible AI chipset architecture that can support changing AI inference and training workloads. This is an area that is well served by NVIDIA, Intel, and Xilinx, but new entrants such as Huawei, Graphcore, and Habana Labs may challenge the status quo.

The second strategy is to target intelligent edge devices and nodes, which to some extent favor suppliers active in the consumer electronics space. Chipset suppliers like Qualcomm and MTK have a natural advantage here. Vendors that design their own AI chipsets, such as Apple, Huawei, and Samsung , have also begun to expand their AI-enabled product portfolios for consumer devices.

The final strategy is to target low-cost and battery -powered end devices that have minimal computing power and a long lifespan. These devices are often deployed in smart cities, smart buildings, smart transportation, and utilities, all of which rely on public Ethernet or low-power wide area networks (LPWAN) for connectivity. These "very edge" devices require lighter AI implementations, an approach often referred to as tiny or thin AI. ABI Research predicts that shipments of these devices will increase from 900,000 units in 2019 to 5.7 million units in 2024, with a CAGR of 45.5%. Traditionally, these devices have relied heavily on more powerful resources such as gateways, premise servers, and public clouds for AI training. Recently, many chipset players have participated in this market by offering AI chipsets with extremely high energy efficiency ratios and lower prices. These companies include GreenWave Technologies, a startup developing AI chipsets using the open source RISC -V architecture; Lat tic Semiconductor, an FPGA chipset supplier; and Syn TI ant, an ASIC supplier specializing in natural language processing .

Advantages The AI ​​chipset market is a highly competitive one. Use cases are becoming increasingly complex and diverse, with new players emerging from the horizon almost every month. The major key players have a strong tradition of building global scale for their chips, even in very decentralized environments. Therefore, vendors, especially newcomers, must have a clear value proposition, a comprehensive software stack and strong support from a partner ecosystem and developer community.