The Global Edge AI Processor Market size is expected to reach $5.5 billion by 2028, rising at a market growth of 15.3% CAGR during the forecast period.
Edge artificial intelligence (edge AI) is a paradigm for creating AI workflows that range from centralized data centers (the cloud) and devices closer to humans and physical objects outside the cloud (the edge). This is in contrast to the increasingly usual approach of developing and running AI applications exclusively in the cloud, which has been dubbed cloud AI.
It also varies from previous AI development methods, in which AI algorithms were created on desktops and then deployed on desktops or specific hardware for tasks like reading check numbers. The edge is frequently defined as a physical object, like a network gateway, smart router, or intelligent 5G cell tower. A better way to grasp the significance of edge is to understand it as a mechanism to extend cloud-based digital transformation practices to the rest of the world.
Analysis occurs in a fraction of a second, which is vital in time-sensitive scenarios. Considering the machines that make up an industrial assembly line. If a robot on the production line is activated at the incorrect moment or too late, the product may be damaged or travel further down the line unprocessed and undisturbed. If the error goes undetected, the incorrect product could end up on the market or cause damage later in the manufacturing process. When the majority of data processing occurs locally, on the edge, a centralized service or data transport will not constitute a barrier.
Large volumes of data are frequently involved in edge AI use cases. Transferring video image data to a cloud service is not a realistic solution if the user needs to process data from a variety of distinct sources at the same time. In a self-driving automobile, there are hundreds of sensors that constantly monitor elements like the vehicle's position and tire rotation speed. Based on the data collected from the sensors, the driving computer can make the appropriate decisions regarding steering, braking, and throttle use automatically.
The major strategies followed by the market participants are Partnerships. Based on the Analysis presented in the Cardinal matrix; Apple, Inc. and Google LLC. are the forerunners in the Edge AI Processor Market. Companies such as Qualcomm, Inc., Intel Corporation and NVIDIA Corporation are some of the key innovators in Edge AI Processor Market.
The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include Apple, Inc., Samsung Electronics Co., Ltd. (Samsung Group), Mythic, Qualcomm, Inc., Huawei Technologies Co., Ltd. (Huawei Investment & Holding Co., Ltd.), Intel Corporation, Google LLC, NVIDIA Corporation, Arm Limited (Softbank Group Corp.) and Advanced Micro Devices, Inc.
By Device Type
Edge artificial intelligence (edge AI) is a paradigm for creating AI workflows that range from centralized data centers (the cloud) and devices closer to humans and physical objects outside the cloud (the edge). This is in contrast to the increasingly usual approach of developing and running AI applications exclusively in the cloud, which has been dubbed cloud AI.
It also varies from previous AI development methods, in which AI algorithms were created on desktops and then deployed on desktops or specific hardware for tasks like reading check numbers. The edge is frequently defined as a physical object, like a network gateway, smart router, or intelligent 5G cell tower. A better way to grasp the significance of edge is to understand it as a mechanism to extend cloud-based digital transformation practices to the rest of the world.
Analysis occurs in a fraction of a second, which is vital in time-sensitive scenarios. Considering the machines that make up an industrial assembly line. If a robot on the production line is activated at the incorrect moment or too late, the product may be damaged or travel further down the line unprocessed and undisturbed. If the error goes undetected, the incorrect product could end up on the market or cause damage later in the manufacturing process. When the majority of data processing occurs locally, on the edge, a centralized service or data transport will not constitute a barrier.
Large volumes of data are frequently involved in edge AI use cases. Transferring video image data to a cloud service is not a realistic solution if the user needs to process data from a variety of distinct sources at the same time. In a self-driving automobile, there are hundreds of sensors that constantly monitor elements like the vehicle's position and tire rotation speed. Based on the data collected from the sensors, the driving computer can make the appropriate decisions regarding steering, braking, and throttle use automatically.
COVID-19 Impact Analysis
The influence of COVID-19 on businesses is altering business paradigms. Every organization is affected by the COVID-19 pandemic. In order to provide a safe working environment, businesses are striving to reconfigure their supply networks. Remote working functionality, remote asset maintenance, and monitoring, plant automation, as well as telehealth are all being implemented by organizations all over the world to reduce pandemic risks. The healthcare business has benefitted immensely from the transition from computers to the cloud to the edge.Market Growth Factors
Enhanced privacy and security
Because there is less data on the cloud, there are fewer tendencies for cyber-attacks. Edge frequently operates in a closed network, making information theft more difficult. A network with several devices is also more difficult to pull down. In general, anything that has a security component should be done on the edge. Considering the intelligent safety monitoring systems in a factory, for example. When devices fail to function properly or people move in an area where they are not authorized to, an alarm should sound before an accident occurs.Increased cost savings
One of the major features, as well as a driving factor, of edge AI processors, is that it saves a significant amount of money for the company. Edge AI processors are very cost-efficient due to lesser requirements. Because of the scalability of analytics in making key decisions, the edge can save a significant cost for a company. In addition to saving time, the edge can conserve bandwidth by reducing the amount of data that needs to be transferred. This also improves the energy efficiency of the equipment.Marketing Restraining Factor:
Increased latency and high bandwidth usage
Latency issues, privacy concerns, and bandwidth constraints are just a few of the concerns that cloud computing encounters. Chips are now so compact that they can conduct advanced computing functions on Edge-enabled devices natively, making Edge computing a must-have in situations where latency and privacy are critical. When a centralized system, such as the cloud, gets overwhelmed with massive amounts of data, latency issues arise. As a result, real-time business requirements may be challenging to meet with cloud computing.
Device Type Outlook
Based on the Device Type, the Edge AI Processor Market is bifurcated into Consumer Devices and Enterprise Devices. This is due to its advantages, which include energy efficiency. Less data is transmitted to and from the cloud as more data is processed at the edge, resulting in lower data latency and energy consumption. A vast number of companies aim to employ Edge computing solutions for energy efficiency monitoring in the next years.Type Outlook
By the Type, the Edge AI Processor Market segregated into Central Processing Unit (CPU), Graphics Processing Unit (GPU), and Application Specific Integrated Circuit (ASIC). Application-specific integrated circuits allow for the creation of whole mechanisms on a single chip, which is expected to drive application-specific integrated circuits' growth. An application-specific integrated circuits are chips that are modified for a particular use rather than being developed for general usage.End Use Outlook
On the basis of End-Use, the Edge AI Processor Market is segmented into Automotive and Transportation, Healthcare, Consumer Electronics, Retail and Ecommerce, Manufacturing, and Others. In terms of volume, consumer electronics dominate the edge AI processor market due to rising technological advancements all over the world. This is due to rising consumer expenditure along with increasing demand for consumer gadgets. Smart wearables, smartphones, and other electronic devices are gaining in popularity.Regional Outlook
Based on Regions, the market is segmented into North America, Europe, Asia Pacific, and Latin America, Middle East & Africa. This is due to a variety of variables, including the greatest production of Edge AI processors in the United States and Canada, as well as a significant consumer base. These reasons are expected to drive significant growth in the regional edge AI processor market in the coming years.Cardinal Matrix-Edge AI Processor Market Competition Analysis
The major strategies followed by the market participants are Partnerships. Based on the Analysis presented in the Cardinal matrix; Apple, Inc. and Google LLC. are the forerunners in the Edge AI Processor Market. Companies such as Qualcomm, Inc., Intel Corporation and NVIDIA Corporation are some of the key innovators in Edge AI Processor Market.
The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include Apple, Inc., Samsung Electronics Co., Ltd. (Samsung Group), Mythic, Qualcomm, Inc., Huawei Technologies Co., Ltd. (Huawei Investment & Holding Co., Ltd.), Intel Corporation, Google LLC, NVIDIA Corporation, Arm Limited (Softbank Group Corp.) and Advanced Micro Devices, Inc.
Recent Strategies deployed in Edge AI Processor Market
Partnerships, Collaborations and Agreements:
- Jun-2022: Advanced Micro Devices joined hands with NIO, a Chinese electric automobile manufacturer. Under this collaboration, NIO is expected to leverage the EPYC processors series of AMD in order to accelerate the AI deep learning training and reduce the product development cycles.
- Nov-2021: Qualcomm Technologies joined hands with Google Cloud, a suite of cloud computing services by Google. With this collaboration, the companies aimed to expedite neural network development along with differentiation for Snapdragon mobile, XR, and ACPC platforms, Snapdragon Ride Platform, and IoT platforms of Qualcomm Technologies by leveraging Google Cloud Vertex AI Neural Architecture Search with Qualcomm Artificial Intelligence Engine. Moreover, Google Cloud Vertex AI NAS is expected to enable Qualcomm Technologies to introduce high-accuracy AI with reduced latency to low-power devices.
- Aug-2021: Samsung came into a partnership with Synopsys, an American electronic design automation company. Following this partnership, Samsung aimed to develop new AI-powered features in its latest range of computer processor designs by leveraging Synopsys software to design its Exynos chip range.
- Apr-2021: NVIDIA entered into a partnership with Cloudflare, a security and performance company. Following this partnership, the companies aimed to introduce AI to the edge at scale with the integration of accelerated computing technology of NVIDIA and Cloudflare’s edge network in order to develop a significant platform to allow developers to deploy applications with custom or pre-trained machine learning models quickly.
- Nov-2020: Qualcomm teamed up with Microsoft, an American multinational technology corporation. This collaboration aimed to improve machine learning and the artificial intelligence developer experience. In addition, the collaboration is expected to also benefit organizations that employ Microsoft's AI and Azure solutions incorporated with IoT processors and other AI products of Qualcomm.
- Aug-2020: Intel collaborated with Altran, a global innovation and engineering consulting firm. Following this collaboration, the companies is expected to develop an open-edge AI platform for telecom service providers across Europe. Moreover, the new solution is expected to enable operators to jointly provide edge services irrespective of location as well as the platform.
Product Launches and Product Expansions:
- May-2022: Qualcomm expanded its 5G and edge artificial intelligence systems with the addition of the Qualcomm Robotics RB6 platform and RB5 AMR Reference Design. With this product expansion, the company aimed to bring improvements in AI as well as 5G technologies in order to support safer, smarter, and more advanced innovations.
- May-2022: Intel rolled out Gaudi2, a new chip. Through this launch, the company aimed to increase its focus on artificial intelligence computing to expand its footprint in the AI chip market.
- Aug-2021: Qualcomm launched Gloria AI Edge Box. The new Qualcomm Cloud AI 100 inference accelerator-featured on-premise solution is an AI-built machine vision platform, which is compatible with 24 high-definition cameras for several video analytics applications.
- Jun-2021: Mythic released the M1076 Analog Matrix Processor. The new product aimed to deliver a seamless combination of the best-in-class scalability, performance, as well as power efficiency. In addition, the new product also features an ultra-compact 22mm x 30mm PCIe M.2 A+E Key card for applications, like the card for space-constrained embedded edge AI.
Scope of the Study
Market Segments Covered in the Report:
By Device Type
- Consumer Devices
- Enterprise Devices
- Central Processing Unit (CPU)
- Graphics Processing Unit (GPU)
- Application Specific Integrated Circuit (ASIC)
- Consumer Electronics
- Automotive & Transportation
- Manufacturing
- Retail & Ecommerce
- Healthcare
- Others
- North America
- US
- Canada
- Mexico
- Rest of North America
- Europe
- Germany
- UK
- France
- Russia
- Spain
- Italy
- Rest of Europe
- Asia Pacific
- China
- Japan
- India
- South Korea
- Singapore
- Malaysia
- Rest of Asia Pacific
- LAMEA
- Brazil
- Argentina
- UAE
- Saudi Arabia
- South Africa
- Nigeria
- Rest of LAMEA
Key Market Players
List of Companies Profiled in the Report:
- Apple, Inc.
- Samsung Electronics Co., Ltd. (Samsung Group)
- Mythic
- Qualcomm, Inc.
- Huawei Technologies Co., Ltd. (Huawei Investment & Holding Co., Ltd.)
- Intel Corporation
- Google LLC
- NVIDIA Corporation
- Arm Limited (Softbank Group Corp.)
- Advanced Micro Devices, Inc.
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Table of Contents
Chapter 1. Market Scope & Methodology
Chapter 2. Market Overview
Chapter 3. Competition Analysis - Global
Chapter 4. Global Edge AI Processor Market by Device Type
Chapter 5. Global Edge AI Processor Market by Type
Chapter 6. Global Edge AI Processor Market by End Use
Chapter 7. Global Edge AI Processor Market by Region
Chapter 8. Company Profiles
Companies Mentioned
- Apple, Inc.
- Samsung Electronics Co., Ltd. (Samsung Group)
- Mythic
- Qualcomm, Inc.
- Huawei Technologies Co., Ltd. (Huawei Investment & Holding Co., Ltd.)
- Intel Corporation
- Google LLC
- NVIDIA Corporation
- Arm Limited (Softbank Group Corp.)
- Advanced Micro Devices, Inc.
Methodology
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