The edge artificial intelligence (AI) chipset market is expected to exceed the cloud AI chipset market for the first time in 2025, propelled by the increasing focus on low latency, data privacy, and the availability of low-cost and ultra-power-efficient AI chipsets. According to global tech market advisory firm, ABI Research, the edge AI chipset market will reach US$12.2 billion in revenues, outpacing the cloud AI chipset market, which will reach US$11.9 billion in 2025.
At present, the cloud is the center of AI. Most AI training and inference workloads happen in the public and private clouds. Traditionally, the centralization of these workloads in the cloud brings the benefits of flexibility and scalability. However, the industry has witnessed a shift in the AI paradigm. Driven by the need for privacy, cybersecurity, and low latency, there is an emergence of performing AI training and inference workloads on gateways, devices, and sensors. Recent advancements in key domains, including connectivity to cloud computing, new AI learning architecture, and high-performance computational chipsets, have played a critical role in this shift.
“As enterprises start to look for AI solutions in the areas of image and object recognition, autonomous material handling, predictive maintenance, and human-machine interface for end devices, they need to resolve concerns around data privacy, power efficiency, low latency, and strong on-device computing performance,” explains Lian Jye Su, Principal Analyst at ABI Research. “Edge AI will be the answer to this. By integrating an AI chipset designed to perform high-speed inference and quantized federated learning or collaborative learning models, edge AI brings task automation and augmentation to device and sensor levels across various sectors. So much that it will grow and surpass the cloud AI chipset market in 2025.”
This is a prime opportunity for chipset vendors with a diverse range of product offerings to capitalize and shine, instead of relying solely on a particular niche. Intel is a prime example. In 2019, the chipset conglomerate witnessed strong growth in Mobileye, its ADAS chipset subsidiary, and overtook NVIDIA as the leading-edge AI vendor. Intel is expected to continue to see high demands for its cloud AI chipset and experience strong demand for its Mobileye, Movidius, and FPGA product solutions.
In the consumer market, COVID-19 has disrupted the demand for many smart devices, notably smartphones, smart home, and wearables, which has impacted the deployment of AI accelerators targeting these devices. At the same time, implementation in industrial manufacturing, retail, and other verticals have been postponed or put on hold.
“Nonetheless, ABI Research expects the market to rebound in 2022. It is important to note that the impact on the chipset supply chain has been relatively minimal since fabrication factories in Singapore and Taiwan remained operational during the outbreak,” Su points out. Vendors of key connectivity technologies such as 5G, Wi-Fi 6, and autonomous solutions such as autonomous vehicles see minimal impact to their product roadmaps. They are continuing with their trials and deployments, foretelling a quick rebound in demand for edge AI chipset beyond 2022. “Catalyzing many other emerging technologies, edge AI will pave the way for a variety of new business opportunities in the consumer and enterprise segments,” Su concludes.
These findings are from ABI Research’s Artificial Intelligence and Machine Learning market data report. This report is part of the company’s Artificial Intelligence and Machine Learning research service, which includes research, data, and analyst insights. Market Data spreadsheets are composed of in-depth data, market share analysis, and highly segmented, service-specific forecasts to provide detailed insight where opportunities lie.
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