PUBLISHER: Stratistics Market Research Consulting | PRODUCT CODE: 1235946
PUBLISHER: Stratistics Market Research Consulting | PRODUCT CODE: 1235946
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According to Stratistics MRC, the Global Edge AI Hardware Market is accounted for $1056 million in 2022 and is expected to reach $3281 million by 2028 growing at a CAGR of 20.8% during the forecast period. Edge AI hardware is a collection of devices and equipment used to process and power AI-based robots and devices. These devices and equipment are used to integrate and improve artificial intelligence device processing by processing data in the device itself. In this process, no cloud computing or cloud systems are required. This ability allows the devices to make their own decisions.
According to Seagate, 44% of the data created in the core and edge will be driven by analytics, artificial intelligence, and deep learning, as well as by an increasing number of IoT devices feeding data to the enterprise edge, where it creates key players in the market with ample opportunities to expand.
Smart cities are complex structures that integrate various systems to support the life cycle of a human. These systems include smart healthcare, smart transportation, smart manufacturing, smart buildings, smart energy, and smart farming, to name a few. Smart cities are gradually adopting AI technologies as they become more prevalent across various industries. As smart cities become more common, more people are becoming interested in the concept of a smart home. The use of automated services in people's daily lives is expected to increase as more people move to cities. Smart homes are transitioning from a luxury to a necessity.
In edge AI, pre-trained ML models are currently used for inference. These models automatically adjust based on user data and requirements. Training a model requires a significant amount of computer power, and because edge AI has limited access to training data, it is more susceptible to uncertainty and unpredictability. Furthermore, while edge AI can perform small transfer learning tasks, it cannot perform deep learning tasks. Concerns about cloud computing include latency issues, privacy concerns, and bandwidth limitations. Such factors hinder the market growth.
With the introduction of 5G networks, IT and telecoms are collaborating to deliver new capabilities for high-end apps and reduce network latency. Using virtualization and software-defined networking principles, the 5G network enables the development of data centers at edge modules as well as the implementation of industry-specific networks in a single environment. Autonomous vehicles, industrial automation, surgery, and robotics all require extremely low latency. The emergence of 5g networks across various applications is expected to increase the volume of data transferred to data centers, which increases the need for edge networks.
Edge AI hardware can be costly to develop and manufacture, limiting adoption, particularly for small and medium-sized businesses. To prevent unauthorised access and ensure data privacy, edge AI hardware must be secure, which can be difficult given the complexity of AI systems. However, with ongoing research and development efforts, these issues are expected to be overcome, resulting in continued growth and adoption of Edge AI hardware in a variety of industries.
The COVID-19 breakout has had a significant impact on the operations of the production and manufacturing industries, stifling the growth of the edge AI hardware market. Furthermore, the COVID-19 pandemic has had an impact on the electronics industry, as production facilities have been shut down, resulting in an increase in demand for electronics and semiconductor products across industries. It has a significant impact on European manufacturing and Chinese exports, which may stifle market growth.
The smartphones segment has the highest revenue share in the edge AI hardware market and is expected to witness largest share during the forecast period due to the increasing demand for smartphones. Edge AI improves smartphone imaging and photography capabilities, as well as power efficiency and security. Until now, most AI-related processing tasks on mobile apps and assistants, such as prediction, detection, pattern matching, and classification, have been performed primarily in the cloud. However, with AI processors built into phones, these AI tasks could be performed directly on the device, even without any connectivity. Such aspects are propelling the segment's growth.
The Training segment is estimated to witness the highest CAGR over the projection period. The process of developing an algorithm that will be used to infer the output is known as training. Machine learning models are trained to understand a data set and act on new data. Because mobile devices lack high-performance computing capabilities, ML models are trained in the cloud. Furthermore, on-device training will be limited to specific devices such as automotive systems and robots and will not be required for all applications. Given its benefits, on-device training is expected to grow in the coming years.
North America had the highest revenue share and is expected to maintain its lead throughout the forecast period due to the increasing number of IoT devices, the growing need for faster processing devices, increased government funding, and the region's strong technical base. The United States is the largest revenue generator for North American players dealing in edge AI hardware. The United States is a key market for AI application processors due to the country's high demand for smartphones, smart home appliances, and advanced products such as IoT devices, wearable electronics, and vehicles with high-security features.
Due to the advent of 5G in the region and the increasing number of IoT-incorporated devices, the Asia-Pacific region is expected to experience the highest growth rate in the global edge AI hardware market. The growing smartphone penetration in China, Japan, India, and South Korea is expected to boost AI hardware market adoption. China is the region's largest market, followed by Japan. Moreover, the presence of several significant vendors in the automobile, electronics, and semiconductor industries that are investing heavily in AI technology is driving the growth of the region's edge AI hardware market.
Some of the key players in Edge AI Hardware market include: Alibaba Group Holding Limited, Amazon.com Inc., Apple Inc., Continental AG, Denso Corporation, Google LLC (Alphabet Inc.), Huawei Technologies Co., Ltd, Imagination Technologies, Intel Corporation, International Business Machines Corporation (IBM), KALRAY Corporation, MediaTek Inc., MICROSOFT CORPORATION, NVIDIA CORPORATION, Qualcomm Technologies, Inc., Robert Bosch GmbH Samsung Electronics and Xilinx Inc.
In November 2022, Network solutions provider Lumen Technologies began expanding its portfolio of Edge Computing Solutions into the Asia-Pacific Region, which will include its Edge Bare Metal pay-as-you-go hardware solution for servers, taking advantage of sites in Singapore and Japan.
In October 2022, Kneron bagged USD 50 million in funding for next-gen AI hardware solutions. The company plans to use the funds to accelerate its research and development to produce next-gen AI inference modules. Kneron anticipates increased adoption of on-device edge AI technology in the future. This involves placing AI computing power onto devices that include hardware rather than within cloud software.
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Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.