EMBEDDED COMPUTING MARKET SIZE AND SHARE ANALYSIS - GROWTH TRENDS AND FORECASTS (2024-2031)

Embedded Computing Market Size and Trends

The global embedded computing market is estimated to be valued at US$ 112.45 Bn in 2024 and is expected to reach US$ 174.38 Bn by 2031, exhibiting a compound annual growth rate (CAGR) of 6.5% from 2024 to 2031.

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The growing adoption of Internet of Things (IoT) devices across various industries is driving the need for embedded computing systems. Increased investments by automotive OEMs to enhance driver assistance, vehicle connectivity, and powertrain electrification are also fueling the demand for embedded computing.

The global embedded computing market is expected to be driven by the increasing focus on enhancing performance and functionality of embedded systems. Lightweight, low-power, and high-performance embedded technologies are gaining popularity among end users. Further, the integration of artificial intelligence into various embedded devices is widening the scope of applications for embedded computing systems. Advanced technologies, such as functional safety, 5G connectivity, and edge computing, provide opportunities for market participants over the forecast period.

Connected devices driving growth

The proliferation of connected devices is a major driver propelling the global embedded computing market forward. As technologies like IoT, industrial automation, and telematics continue to expand their footprint, the need for intelligent edge devices with embedded computing capabilities is growing rapidly. An ever-increasing array of ordinary items such as home appliances, vehicles, industrial machines, and medical equipment are being outfitted with various sensors and communication modules that require an onboard computing system to facilitate connectivity and data collection.

Embedded systems allow these devices to gain intelligence by enabling functions like real-time monitoring, remote access and control, data collection and analytics. This is opening up new possibilities for device optimization, predictive maintenance, process automation, and others. Manufacturers across many industries now rely on embedded computing to digitalize their offerings and provide differentiated value-added services. Consumer electronics is another major sector benefiting from this trend, with smart TVs, wearable and other connected consumer products driving significant embedded design wins. Even traditionally non-technical industries are embracing embedded computing and IoT to modernize operations and business models.

The proliferation of 5G networks and ongoing advancements in wireless technologies are serving to supercharge this growth trajectory. Higher bandwidth, lower latency and broader coverage offered by 5G make it possible to connect many more types of devices than before. This will generate even stronger demand for embedded systems customized for specific endpoints, devices and gateways that can take advantage of the improved connectivity. As 5G networks mature globally in the coming years, it is estimated that tens of billions of connected devices will come online, further expanding the market potential.

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Developments in AI and edge computing

Embedded computing is essential for harnessing the potential of emerging technologies like artificial intelligence and edge computing. Advanced data processing that used to occur exclusively in the cloud is now shifting to distributed edge devices due to the capabilities of modern embedded systems. This distributed computing model is opening up exciting new use cases while also addressing challenges around bandwidth constraints and latency issues associated with cloud-based approaches.

Embedded AI aims to bring local, real-time data analytics, and decision making where it is needed most - right at the source of data generation. This brings advantages like reduced response times, elimination of reliance on cloud connectivity, and enhanced privacy and security by minimizing data transfers. Such systems rely heavily on specialized embedded platforms optimized for running AI/ML workloads locally. Powerful yet efficient embedded processors, FPGAs and ASICs are being increasingly used alongside dedicated AI accelerators and neural networking software.

Similarly, edge computing architectures depend on embedded infrastructure being located close to the IoT endpoints. Embedded systems serve as the edge computing gateways that collect and pre-process IoT data before sending relevant analytics and insights to the cloud. This results in lower cloud workloads and network usage. Both AI and edge computing are motivating OEMs to design highly customized embedded solutions tailored for their specific application requirements. The combination of these factors has placed embedded computing at the forefront of next wave technologies like Industry 4.0, self-driving vehicles and predictive maintenance. Going forward, its crucial role in enabling AI everywhere will continue bolstering demand.