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The global AI accelerator market is a rapidly growing market that is expected to reach USD 142.1 billion by 2030. The market is being driven by the increasing demand for AI acceleration from a wide range of industries, including data centers, cloud computing, consumer electronics, automotive, healthcare, finance, manufacturing, and retail.
AI accelerators are specialized hardware devices that are designed to speed up the performance of AI applications. AI accelerators can be used to accelerate a wide range of AI tasks, such as machine learning, image processing, natural language processing, and computer vision.
The North American region is the largest market for AI accelerators, followed by the Asia Pacific region. The data center market is the largest end-user market for AI accelerators, followed by the cloud computing market. The GPU segment is the largest product segment in the global AI accelerator market, followed by the FPGA segment. The machine learning segment is the largest application segment in the global AI accelerator market, followed by the image processing segment.
AI accelerators constitute a distinct category of processors explicitly crafted to handle artificial intelligence workloads. They bring forth a set of notable advantages by harnessing hardware acceleration and parallel processing, evident in their applications across a spectrum of devices, from personal computers, smartphones, and video game consoles to camera systems and various smart devices.
These AI accelerators are seamlessly integrated into computing devices, which can encompass a system-on-a-chip (SoC), a central processing unit (CPU), or even a graphics processing unit (GPU). Their primary objective revolves around enhancing the efficiency and effectiveness of tasks related to artificial intelligence (AI).
Typically, AI accelerators feature specialized circuits, instructions, or dedicated processing units meticulously designed to expedite AI workloads. This acceleration translates into swifter AI computations and improved energy efficiency, significantly enhancing the overall performance of AI tasks.
The Global AI Accelerator market accounted for $XX Billion in 2023 and is anticipated to reach $XX Billion by 2030, registering a CAGR of XX% from 2024 to 2030. The Global Artificial Intelligence Accelerator market is poised to demonstrate substantial growth throughout the forecast period spanning from 2023 to 2030.
In the year 2022, the market exhibited consistent growth, and this momentum is expected to escalate due to the increasing implementation of strategic initiatives by key industry players. Notably, North America, particularly the United States, is slated to maintain a pivotal role that cannot be underestimated.
Any alterations in the United States’ approach could significantly impact the developmental trajectory of the Artificial Intelligence Accelerator market. Forecasts suggest a substantial growth trajectory for the North American market during the stipulated forecast period. The region is anticipated to witness remarkable growth owing to the widespread adoption of cutting-edge technology and the prominent presence of major industry stakeholders, creating abundant growth prospects within the market.
Europe also holds a critical position in the global market, showcasing a remarkable growth rate in Compound Annual Growth Rate (CAGR) during the forecast period from 2023 to 2030. Despite the intense competition within this domain, the global recovery trend remains evident, fueling investor optimism and attracting new investments to the sector.
This report concentrates on the global Artificial Intelligence Accelerator market, with a specific focus on North America, Europe, Asia-Pacific, South America, Middle East, and Africa. The segmentation is based on manufacturers, regions, types, and applications.
TRENDS IN THE ARTIFICIAL INTELLIGENCE (AI) ACCELERATOR MARKET
NVIDIA announces a new AI accelerator for data centers: NVIDIA has announced a new AI accelerator for data centers called the NVIDIA A100. The A100 is the world’s fastest AI accelerator and is designed to accelerate a wide range of AI tasks, including machine learning, image processing, natural language processing, and computer vision.
Intel unveils new AI accelerator for edge computing: Intel has unveiled a new AI accelerator for edge computing called the Intel Stratix 10 GX FPGA. The Stratix 10 GX FPGA is designed to accelerate AI applications on edge devices, such as self-driving cars, industrial robots, and smart city devices.
Google announces new AI accelerator for cloud computing: Google has announced a new AI accelerator for cloud computing called the Tensor Processing Unit (TPU) v4. The TPU v4 is the world’s most powerful AI accelerator and is designed to accelerate machine learning applications in the cloud.
Silicon Labs, a prominent provider of connectivity solutions, has unveiled a novel system-on-chip (SoC) that combines support for both short- and long-range wireless networking with an integrated AI and machine learning inference accelerator, tailored for edge applications. This development enables the utilization of devices in scenarios like smart city monitoring and maintenance, where robust and consistent communication options are of paramount importance.
The FG28, equipped with sub-Gigahertz radio modules and compatibility with well-established connectivity protocols such as Amazon Sidewalk, Wi-SUN, and various custom protocols operating within the sub-GHz frequency range, is custom-crafted to serve long-range networks. Silicon Labs asserts that the FG28 hardware platform is well-suited for battery-operated applications and can be deployed in remote and challenging environments.
In another stride towards advanced image recognition for vision AI applications, Renesas Electronics Corporation, a leading provider of cutting-edge semiconductor solutions, has expanded its AI-capable RZ/V Series of microprocessors (MPUs). This expansion includes a new component with dual 64-bit Arm Cortex-A53 cores, delivering robust computational performance at a maximum operating frequency of 1GHz.
What sets the RZ/V2MA apart is its unique low-power Dynamically Reconfigurable Processor (DRP-AI) accelerator, achieving remarkable tera operations per second, per watt (TOPS/W) class performance during vision AI processing.
