Global Edge and Embedded AI Market

MARKET SCOPE:

The global Edge and Embedded AI market is projected to grow significantly, registering a CAGR of 21.7% during the forecast period (2024 – 2032).

Edge and Embedded AI refer to the integration of artificial intelligence (AI) capabilities directly into edge devices or embedded systems, rather than relying on centralized cloud-based processing. This approach brings AI computation closer to the data source, enabling real-time analysis and decision-making at or near the point of data generation. Edge devices include a wide range of hardware, such as sensors, cameras, smartphones, IoT devices, and edge servers, while embedded systems are specialized computing systems designed for specific applications. The goal of Edge and Embedded AI is to enhance the efficiency, responsiveness, and autonomy of AI applications by processing data locally. This is particularly advantageous in scenarios where low latency, real-time processing, and bandwidth optimization are critical, such as in autonomous vehicles, industrial automation, healthcare devices, and smart IoT deployments. Applications requiring quick decision-making, such as autonomous vehicles, augmented reality, and industrial automation, demand low latency. Edge and Embedded AI address this demand by processing data locally, reducing the time it takes to analyze and act on information.

MARKET OVERVIEW:

Driver: Increasing strategies for scalability and distributed architecture is driving the market growth

Edge and Embedded AI systems distribute processing tasks across a network of edge devices. This ensures that computing resources are utilized efficiently, preventing bottlenecks and enabling optimal performance even as the number of connected devices or the complexity of tasks increases. Distributed architecture allows Edge and Embedded AI solutions to adapt to dynamic environments. In scenarios where the number of devices or the nature of tasks can change rapidly, the system can scale up or down seamlessly, ensuring continued efficiency and responsiveness. By distributing processing tasks, Edge and Embedded AI reduce the dependency on centralized processing. This is beneficial in environments with limited or intermittent connectivity to cloud servers, ensuring that AI applications can continue functioning independently.

Opportunities: Growing consumer needs for real time processing applications is anticipated for the market growth in the upcoming years.

In autonomous vehicles, split-second decisions are vital for ensuring the safety of passengers and pedestrians. Edge AI processes data from sensors like cameras and LiDAR on-board, allowing the vehicle to quickly interpret and respond to its environment without relying solely on distant cloud processing. Industrial automation relies on precise and rapid control of machinery and processes. Edge AI in manufacturing environments enables real-time monitoring, quality control, and predictive maintenance, contributing to increased efficiency and reduced downtime. AR and VR applications require low latency to deliver a seamless and immersive user experience. Edge AI allows for on-device processing of sensory data, ensuring that augmented or virtual content is rendered quickly and accurately, enhancing the overall user experience. Various IoT devices, from smart home gadgets to industrial sensors, benefit from low-latency processing at the edge. For instance, a smart security camera equipped with Edge AI can analyze video footage locally, triggering alerts or actions in real-time, without the need for constant communication with a centralized server. Healthcare applications, such as wearable devices for monitoring patient health, require quick analysis of physiological data. Edge AI enables these devices to process data locally, allowing for timely detection of anomalies and providing healthcare professionals with real-time insights.

COVID IMPACT:

The need for remote monitoring and telehealth solutions increased during the pandemic. Edge and Embedded AI technologies played a crucial role in developing remote patient monitoring devices, wearable health trackers, and AI-assisted diagnostics, allowing healthcare professionals to remotely monitor patients and make timely decisions. Healthcare facilities faced challenges in maintaining and managing medical equipment. Edge AI applications for predictive maintenance became more crucial in ensuring the availability of critical healthcare infrastructure, helping hospitals proactively address potential equipment failures. The pandemic highlighted the importance of resilient supply chains. Edge and Embedded AI solutions were employed to enhance visibility and efficiency in supply chain operations. These technologies facilitated real-time monitoring, demand forecasting, and optimization of logistics to respond to disruptions caused by the pandemic.

SEGMENTATION ANALYSIS:

Hardware segment is anticipated to grow significantly during the forecast period

Edge computing devices serve as the foundation for Edge and Embedded AI. These devices include edge servers, gateways, and edge computing platforms equipped with processors, memory, and storage to run AI workloads locally. Specialized hardware components, such as System-on-Chip (SoC) solutions and edge processors, are designed to efficiently handle AI computations. These processors often have dedicated accelerators (like GPUs, TPUs, or NPUs) optimized for machine learning workloads. APUs integrate both traditional central processing units (CPUs) and specialized AI accelerators on a single chip. These units provide a balance of general-purpose computing and AI-specific processing capabilities for edge devices.

The Manufacturing segment is anticipated to grow significantly during the forecast period

Edge AI is utilized for predictive maintenance of machinery and equipment on the manufacturing floor. By analyzing real-time data from sensors embedded in machines, AI algorithms can predict potential failures, enabling proactive maintenance and minimizing downtime. Embedded AI systems are deployed for quality control and inspection processes. Vision systems and sensors equipped with AI capabilities can detect defects, anomalies, and deviations in real-time, ensuring high-quality production. Edge AI is employed to optimize manufacturing processes by analyzing data from various sensors and devices. This includes fine-tuning parameters, adjusting production schedules, and optimizing energy consumption to improve overall operational efficiency. Edge AI is integrated into robotic systems for real-time decision-making on the factory floor. This enhances the capabilities of robots for tasks such as material handling, assembly, and even collaborative work with human operators.

REGIONAL ANALYSIS:

The North American region is set to witness significant growth during the forecast period

Edge and Embedded AI refers to the deployment of artificial intelligence (AI) capabilities directly on edge devices or embedded systems, rather than relying on centralized cloud-based processing. This approach brings AI computation closer to the data source, reducing latency, enhancing privacy, and allowing for real-time processing in various applications. In North America, the adoption of Edge and Embedded AI has been growing across multiple industries. North America has seen a significant proliferation of IoT devices, ranging from smart home gadgets to industrial sensors. Edge AI is integrated into these devices to enable local data processing, reducing the need for constant communication with cloud servers. Edge AI plays a crucial role in smart cities initiatives across North America. By embedding AI capabilities in surveillance cameras, traffic management systems, and environmental sensors, cities can analyze data locally and respond in real-time to various situations.

COMPETITIVE ANALYSIS:

The global Edge and Embedded AI market is reasonably competitive with mergers, acquisitions, and Component launches. See some of the major key players in the market.

STM Electronics

• In 2021, French software firm Cartesiam was purchased by European semiconductor manufacturer STMicroelectronics. With the assistance of Cartesiam, STMicroelectronics was able to improve its AI strategy and expand its technology portfolio through this acquisition.

Intel Corporation

• In September 2023, Intel Collaborates with Partners to Promote and Expand the Market for End-to-End Private Networks. Globally, private 5G solutions powered by Intel are being implemented in partnership with AWS, Cisco, NTT DATA, Ericsson, and Nokia.

ADLINK Technology Inc.

Alphabet Inc.

AMAZON INC.

Gorilla Technology Group

International Business Machines Corporation

Microsoft Corporation

Nutanix, Inc.

Synaptics Incorporated

Viso.ai

SCOPE OF THE REPORT:

By Component

• Hardware
• Software
• Edge - cloud Infrastructure
• Low – Expansion Nickel Alloys
• Other Components

By End - User

• Consumer Electronics
• Smart Cities
• Manufacturing
• Government
• Automotive
• Others

By Region

• North America (the United States & Canada)
• Europe (Germany, UK, France, Spain, Italy, and the Rest of Europe)
• Asia Pacific (China, Japan, India, and Rest of Asia Pacific)
• Rest of the World (the Middle East & Africa, and Latin America)

KEYE REASONS TO PURCHASE THIS REPORT:

It provides a technological development map over time to understand the industry’s growth rate and indicates how the Edge and Embedded AI market is evolving.

The report offers a dynamic method to various factors that drive or restrain the growth of the market and specifies which Edge and Embedded AI submarket will be the main driver of the overall market from 2024 to 2032.

It renders a definite analysis of changing competitive dynamics and stipulates the leading players and what are their prospects over the forecast period.

It builds a nine-year estimate based on how the market is predicted to grow and shows what will market shares of the global region change by 2032 and which country will lead the market in 2032. Show more