Wearables in the device segment are expected to witness the highest growth rate during the forecast period.

The growing popularity of smart wearable devices, including smartwatches, fitness trackers, and healthcare monitors, drives demand for edge AI hardware tailored for these applications. Wearables are increasingly integrating AI capabilities for real-time data processing and personalized user experiences. Moreover, edge AI hardware enables wearables to perform AI inference and analytics locally on the device, reducing reliance on cloud services for processing. This localized processing enhances responsiveness, preserves user privacy, and conserves battery life by minimizing data transmission to the cloud.

The smart home segment is expected to hold the second largest market share in the edge AI hardware market during the forecast period.

Edge AI hardware plays a pivotal role in the smart home ecosystem, enabling devices to automate tasks, monitor energy usage, adjust settings based on user preferences, and provide personalized recommendations. AI-powered virtual assistants, such as Siri, Alexa, Google Assistant, and Bixby, are embedded in various consumer electronics products. Edge AI hardware enables these devices to process natural language, perform voice recognition, and execute commands locally without continuous internet connectivity.

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The market for edge AI hardware consuming 1–3 W is expected to hold the largest market size during the forecast period.

Edge AI hardware in the 1–3W power range strikes a balance between performance and power efficiency, making it suitable for a wide range of edge computing applications. This power consumption level allows for efficient processing while minimizing energy consumption, crucial for battery-operated and resource-constrained devices, such as smartphones, consume power ranging from 1 to 3 watts. Smartphones are powered by batteries with limited size and capacity. The energy efficiency of such devices is very important, and the power consumption of such devices is critical considering the ever-increasing functionalities.

Asia Pacific is expected to hold the largest share of the edge AI hardware market during the forecast period.

Asia Pacific is home to a significant manufacturing and industrial base, along with the strong presence of automobile, electronics, and semiconductor companies in China and Japan, drives the growth of the edge AI hardware market in the Asia Pacific region. However, these companies require edge AI solutions for predictive maintenance, quality control, supply chain optimization, and robotics. Edge AI hardware plays a critical role in enhancing operational efficiency and productivity in these sectors. Moreover, Applications such as smartphones, industrial robots, and automobiles have huge potential. With the growing penetration of smartphones in China, Japan, India, and South Korea, the adoption of AI-enabled smartphones in Asia Pacific is expected to increase in the coming years.

Key Players

Leading players in the edge AI hardware market include Qualcomm Technologies, Inc. (US), Huawei Technologies Co., Ltd. (China), SAMSUNG (South Korea), Apple Inc. (US), MediaTek Inc. (Taiwan), Intel Corporation (US), NVIDIA Corporation (US), IBM (US), Micron Technology, Inc. (US), and Advanced Micro Devices, Inc. (US). Meta (US), Tesla (US), Google (US), Microsoft (US), Imagination Technologies (UK), Cambricon (China), Tenstorrent (Canada), Blaize (US), General Vision, Inc (US), Mythic (US), Zero ASIC Corporation (US), Applied Brain Research, Inc. (Canada), Horizon Robotics (China), Ceva, Inc. (US), Graphcore (UK), SambaNova Systems, Inc. (US), HAILO (Israel), and Veridify Security Inc. (US) are few other key companies operating in the edge AI hardware market.

Edge AI Hardware: A Peek into the Future

The Edge AI hardware market is on a fast track to revolutionize how we interact with technology. Here’s a glimpse into what the future holds for this exciting field:

Advancements in Chip Design: Moore’s Law isn’t dead yet! We can expect significant leaps in processing power for Edge AI chips, enabling them to handle even more complex tasks at the edge. This paves the way for more sophisticated applications like real-time object recognition and natural language processing on devices.

Focus on Security: As the reliance on edge devices grows, robust security solutions will be paramount. Expect advancements in hardware-based security features and encryption protocols to safeguard sensitive data processed on the edge.

Standardization Efforts: The fragmented nature of the Edge AI landscape is hindering development. We can anticipate increased collaboration among chipmakers, software developers, and industry leaders to establish standardized hardware and software platforms. This will streamline development and accelerate the adoption of Edge AI solutions.

AI on the Move: The rise of 5G connectivity will unlock the potential ofEdge AI in mobile applications. Expect to see smarter wearables, autonomous vehicles that make real-time decisions based on their surroundings, and even drones that can analyze data on the fly.

The Rise of TinyML: Tiny Machine Learning (TinyML) is an emerging technology enabling even smaller devices with limited processing power to run basic AI models. This will further expand the reach of Edge AI, allowing even resource-constrained devices to leverage the power of AI.

The Democratization of Edge AI: With advancements in chip design and the emergence of cloud-based development tools, cutting-edge AI applications will become more accessible. This will empower businesses of all sizes to leverage the power of Edge AI and unlock new possibilities.

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