The overall business technology market experienced significant growth last year. However, 2020 was a challenging year for edge Artificial Intelligence (AI) vendors. Both market demand and deployment have slowed due to the global COVID-19 pandemic.
Anticipating the growing needs of AI processing at the edge, even public cloud vendors like AWS, Microsoft, and Google are introducing hardware and software solutions and forming industrial alliances and partnerships that target edge AI development and deployment.
The post-Covid-19 recovery can also be seen in recent revenue growth and funding activities of edge AI chipset vendors. Although the automotive market suffered some setbacks in 2020, Intel’s Mobileye reported total revenue of $967 million -- that's a big increase for the Advanced Driver-Assistance Systems (ADAS) vendor.
According to the ABI assessment, Horizon Robotics, and ECARX -- two automotive-focused Chinese edge AI chipset startups -- have raised $750 million and $200 million respectively in 2021, indicating expectations for strong future performance.
Another key trend in edge AI is Tiny Machine Learning (TinyML). The ability to embed a small machine learning model in ultra-low-power devices has opened up new possibilities, enabling smart connected sensors and IoT devices to make decisions and take action based on soundwaves, temperature, pressure, vibration, and other time-series data sources.
Traditional microcontroller (MCU) vendors such as NXP, ST Microelectronics, and Renesas are partnering with the AI software and service provider ecosystem, to assist edge AI developers that do not have embedded system design expertise to deploy TinyML solutions.
Other semiconductor technology vendors are introducing ultra-low-power chipsets or proprietary machine learning models that are highly efficient in power consumption and memory footprint.
Not surprisingly, the ability to create developer-friendly software and app platforms -- and the best ecosystem with third-party vendors -- will be essential to accelerate the adoption of edge AI.
Outlook for Edge AI Applications Growth
"These vendors offer edge machine learning operation (MLOps) platforms that facilitate the entire development and deployment process, starting from data collection and processing to model training, optimization, and monitoring," Su concludes.
Many vendors introduce advanced machine learning model compression and quantization techniques that enable large AI deep learning models to shrink in size while maintaining their accuracy and performance.
Compared to the cloud AI chipset market that experienced 68 percent year-on-year growth in 2020, the edge AI chipset market only grew by 1 percent during the same period.
Regardless, the market is expected to bounce back. According to the latest worldwide market study by ABI Research, the edge AI chipset market will reach $28 billion in 2026, with a CAGR of 28.4 percent between 2021 and 2026.
Regardless, the market is expected to bounce back. According to the latest worldwide market study by ABI Research, the edge AI chipset market will reach $28 billion in 2026, with a CAGR of 28.4 percent between 2021 and 2026.
Edge Artificial Intelligence Market Development
"The demand for edge AI is not going away anytime soon. Edge AI devices can process raw data locally, reducing the reliance on constant cloud connectivity. Consumers appreciate the enhanced user experience brought by low latency and data privacy," said Lian Jye Su, principal analyst at ABI Research.
"The demand for edge AI is not going away anytime soon. Edge AI devices can process raw data locally, reducing the reliance on constant cloud connectivity. Consumers appreciate the enhanced user experience brought by low latency and data privacy," said Lian Jye Su, principal analyst at ABI Research.
At the same time, more and more enterprises are seeking ways to make sense of valuable asset data. They recognize the importance of edge AI in key applications -- such as predictive maintenance, defect inspection, and surveillance.
Anticipating the growing needs of AI processing at the edge, even public cloud vendors like AWS, Microsoft, and Google are introducing hardware and software solutions and forming industrial alliances and partnerships that target edge AI development and deployment.
The post-Covid-19 recovery can also be seen in recent revenue growth and funding activities of edge AI chipset vendors. Although the automotive market suffered some setbacks in 2020, Intel’s Mobileye reported total revenue of $967 million -- that's a big increase for the Advanced Driver-Assistance Systems (ADAS) vendor.
According to the ABI assessment, Horizon Robotics, and ECARX -- two automotive-focused Chinese edge AI chipset startups -- have raised $750 million and $200 million respectively in 2021, indicating expectations for strong future performance.
Another key trend in edge AI is Tiny Machine Learning (TinyML). The ability to embed a small machine learning model in ultra-low-power devices has opened up new possibilities, enabling smart connected sensors and IoT devices to make decisions and take action based on soundwaves, temperature, pressure, vibration, and other time-series data sources.
Traditional microcontroller (MCU) vendors such as NXP, ST Microelectronics, and Renesas are partnering with the AI software and service provider ecosystem, to assist edge AI developers that do not have embedded system design expertise to deploy TinyML solutions.
Other semiconductor technology vendors are introducing ultra-low-power chipsets or proprietary machine learning models that are highly efficient in power consumption and memory footprint.
Not surprisingly, the ability to create developer-friendly software and app platforms -- and the best ecosystem with third-party vendors -- will be essential to accelerate the adoption of edge AI.
Outlook for Edge AI Applications Growth
"These vendors offer edge machine learning operation (MLOps) platforms that facilitate the entire development and deployment process, starting from data collection and processing to model training, optimization, and monitoring," Su concludes.
Many vendors introduce advanced machine learning model compression and quantization techniques that enable large AI deep learning models to shrink in size while maintaining their accuracy and performance.
This frees machine learning models from resource-rich devices, as they can now be deployed across a wide range of devices. Moreover, I believe that the emerging edge computing applications market will experience more predictable growth once 5G wireless services are broadly deployed across the globe.