Skip to main content

Edge Computing Artificial Intelligence Apps Demand

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.

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.

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.

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.

Popular posts from this blog

Digital Transformation Investment at $3.4 Trillion

Business technology leadership matters. Across the globe, more leaders have been pursuing bold Digital Transformation (DX) initiatives with the goal of creating new sources of business value through digital products, services, and experiences. As an additional benefit, the COVID-19 pandemic revealed that digital transformation efforts improve an organization's resilience against global market disruptions. Global DX investment is forecast to reach $3.4 trillion in 2026 with a five-year compound annual growth rate (CAGR) of 16.3 percent, according to the latest worldwide market study by International Data Corporation (IDC). Digital Transformation Market Development "Despite strong headwinds from global supply chain constraints, soaring inflation, political uncertainty, and an impending recession, investment in digital transformation is expected to remain robust," said Craig Simpson, senior research manager at IDC . The benefits of investing in DX technology -- including aut

Artificial Intelligence for National Border Security

National border protection agencies are under pressure to provide the highest level of security in the face of growing threats, such as increasing illegal migration and international terrorism. Now, government agencies are embracing advanced border security technologies to aid in effectively and reliably securing national borders. These solutions look to detect and identify potential threats and prevent them from escalating to a point that may jeopardize security. Security Surveillance Market Development Traditional border security patrols and Closed-circuit Television (CCTV) surveillance systems aren't adequate protection, and agencies must increasingly deploy new solutions to stay ahead of criminals and other potential threats to ensure the safety of a country’s borders. According to the latest market study by Juniper Research, the value of the border security technology market will exceed $70 billion globally in 2027 -- that's rising from $48 billion in 2022. Growing by 47 p

Precision Medicine Spend to Reach $132.3 Billion

Precision Medicine uses molecular info to extract the optimum medical method from diagnostic protocols, by merging the impact of environmental and genetic factors. Data access is essential, with genetic metabolic and clinical data used to build a fuller picture of a patient's biology. Moreover, the primary aim of precision medicine is to design and optimize a pathway for diagnosis, therapeutic intervention, and prognosis, using large biological datasets. Personalized, evidence-based medicine uses stored health data, which includes patient diagnosis, laboratory work, insurance claims, and demographic information. The results enable healthcare providers to predict and prevent some illnesses. Precision Medicine Market Development According to the latest worldwide market study by Juniper Research, the total investment in precision medicine will reach $132.3 billion globally by 2027 -- that's increasing from only $35.7 billion in 2022. The significant market growth of 270 percent is