Edge AI Hardware Market

[200 Pages Report] The edge AI hardware market is projected to grow from USD 24.2 billion in 2024 and is expected to reach USD 54.7 billion by 2029, growing at a CAGR of 17.7% from 2024 to 2029. The rapid growth of Internet of Things (IoT) devices across various industries, including smart homes, industrial automation, healthcare, agriculture, and transportation, is a significant driver for edge AI hardware. Edge AI enables IoT devices to process data locally, make real-time decisions, and reduce reliance on centralized cloud computing.

The objective of the report is to define, describe, and forecast the edge AI hardware market based on Device, power consumption, processor, function, vertical, region.

Edge AI Hardware Market Forecast to 2029

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Edge AI Hardware Market Dynamics

Drivers: Growing demand for real-time data transmission in mission-critical applications

Edge AI hardware enables data to be processed locally at the edge of the network, reducing the time it takes for critical decisions to be made favoring mission-critical applications such as autonomous vehicles, industrial automation, healthcare monitoring, and emergency response systems to gather real-time data processing with minimal latency. Edge AI hardware enhances the reliability and resilience of mission-critical systems by reducing dependence on cloud connectivity. Local processing ensures that applications continue to function even in cases of intermittent or disrupted network connectivity. It also reduces the volume of data transmitted over the network by filtering and processing data locally. This optimizes network bandwidth and reduces congestion, particularly in scenarios where large volumes of data are generated continuously.

Restraint: Associated complications in building edge networks

Establishing edge networks requires a robust and distributed network infrastructure that can handle diverse edge devices and applications. Integrating edge nodes, gateways, and cloud resources while ensuring seamless connectivity and scalability can be complex and resource-intensive. Moreover, edge networks often consist of heterogeneous devices and technologies from different vendors. Ensuring interoperability and compatibility between hardware, software, and communication protocols across the edge ecosystem can be challenging, leading to integration issues and deployment delays.

Opportunity: Optimizing edge AI hardware with generative AI workloads

Generative AI specializes in tasks like creating realistic images, music, or text formats (like poems or code) based on existing data. In contrast, edge AI empowers devices to analyze and interpret data at the source rather than relying solely on the cloud for processing. Generative AI algorithms have specific computational needs. Generative AI can design chips with the right balance of processing power, memory, and specialized hardware accelerators for generative AI tasks, minimizing power consumption at the edge. By optimizing hardware for specific tasks, generative AI can help create smaller, more efficient edge devices ideal for applications with size and power limitations, like wearables or Internet of Things (IoT) devices. Generative AI models often require real-time processing, especially in image and speech generation applications. With its ability to perform computations locally on the device, Edge AI hardware reduces the latency associated with sending data to a remote server for processing. This is crucial for applications requiring real-time responses, such as autonomous vehicles, robotics, and augmented reality.

Challenge: Lack of effective standardization

Edge computing encompasses a wide array of devices, including sensors, gateways, edge servers, and specialized AI accelerators. The lack of standardized interfaces and protocols across these devices complicates integration and interoperability within edge networks. Moreover, edge AI hardware solutions vary in terms of hardware architectures, processing units (e.g., CPUs, GPUs, FPGAs, TPUs), memory configurations, and power consumption profiles. The absence of standardized hardware specifications makes it challenging for developers and solution providers to develop software and applications that can run efficiently across diverse edge devices.

Edge AI hardware Map/Ecosystem:

Inference in the function segment of the edge AI hardware market is expected hold largest market size during the forecast period.

Many edge applications require low latency and real-time processing of data to enable immediate actions and responses. Inference at the edge enables devices to analyze data locally without relying on cloud connectivity, reducing latency and improving responsiveness for time-sensitive applications. Moreover, on-device inference can lead to high responsiveness, which is a major factor in the growth of the market for edge AI hardware products designed to draw inferences.

The GPU in the processor segment of edge AI hardware market to witness highest CAGR during the forecast period.

GPUs are specifically designed to accelerate matrix and vector operations commonly used in AI and machine learning algorithms. They offer high computational throughput and floating-point performance, making them well-suited for AI inference tasks at the edge. Moreover, modern GPUs are highly scalable and configurable, allowing edge AI hardware developers to optimize performance based on specific application requirements. GPUs can be integrated into various edge devices, from smartphones and IoT sensors to edge servers and industrial machines.

The healthcare segment is expected to witness highest growth rate in the edge AI hardware market during the forecast period.

The healthcare industry is increasingly adopting Internet of Things (IoT) devices for patient monitoring, remote care, asset tracking, and facility management. Healthcare applications require immediate insights and responses based on patient data, diagnostic images, sensor readings, and medical records. Edge AI hardware enables healthcare providers to analyze data locally and make critical decisions without relying solely on centralized servers. Moreover, edge AI hardware supports continuous patient monitoring and personalized healthcare services by processing data from wearable devices, medical sensors, and home-based monitoring systems.

Edge AI hardware market in the Asia Pacific estimated to grow at the fastest rate during the forecast period.

Many countries in Asia Pacific, including China, Japan, South Korea, and India, are leading in technology adoption and innovation. These countries have large consumer electronics markets and are investing heavily in AI, driving the demand for edge AI hardware for smart devices and applications. For instance In 2024, according to an article by CNN Business, the South Korean government will invest USD 6.94 billion in artificial intelligence by 2027 as part of efforts to retain a leading global position in cutting-edge semiconductor chips. With this announcement, the government would support establishing a high-profile research center that would work as a "pivot" for the country's R&D activities in the artificial intelligence field.

Edge AI Hardware Market by Region

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Key Market Players

Major vendors in the edge AI hardware companies 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), among others.

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Scope of the Report

Edge AI Hardware Market Highlights

This research report categorizes the edge AI Hardware market based on Device, Power consumption, Processor, Function, Vertical and Region

Recent Developments

• In July 2023, Huawei Technologies Co., Ltd. and other partners officially launched the "Global Alliance on Artificial Intelligence for Industry and Manufacturing" (AIM Global). Led by UNIDO (Austria), AIM Global would integrate public and private partners to foster the use of and innovation around AI in industry and manufacturing.
• In April 2024, Apple plans to revolutionize its Mac lineup with the imminent production of M4 computer processors equipped with cutting-edge AI processing capabilities. According to Bloomberg, citing sources familiar with the matter, the company plans to equip every Mac model with this advanced TECHNOLOGY.
• In January 2024, IBM announced that Korea Quantum Computing (KQC) had engaged IBM to offer IBM's most advanced AI software and infrastructure and quantum computing services. KQC's ecosystem of users will have access to IBM's full stack solution for AI, including Watsonx, an AI and data platform to train, tune and deploy advanced AI models and software for enterprises.

Critical questions answered by this report::

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