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The Edge AI Hardware Market Size is expected to be valued at USD 1.27 Billion in 2023. It is estimated to reach USD 6.90 Billion by 2032, reflecting a substantial CAGR of 20.7% over the forecast period from 2024 to 2032.
Edge Al is an advanced type of device used for processing and intelligently designed robots and devices. This tool is used to integrate and optimize processing intelligence by processing data on the device itself. Cloud computing is not required for this process. These factors enable the device to make its own decisions. Edge-based AI processing enables workflow on a device that reduces data flow over long distances which is a great help for companies to analyze data in less time or based on real time.
KEY DRIVERS:
The number of intelligent applications is rapidly increasing.
Data storage and operating costs are reduced.
Demand for low latency and real-time processing on edge devices is increasing.
This revolves around the efficiency improvements and cost-saving advantages linked to diminishing data storage and operational expenses. As technology progresses, there is an increasing inclination toward refining data storage solutions and operational costs, contributing to enhanced resource management for businesses and organizations. Innovations in storage technologies, such as more effective compression algorithms, scalable cloud storage solutions, or advancements in data deduplication techniques, may lead to a decrease in data storage costs. Similarly, optimizing operational costs entails simplifying processes, embracing automation, and adopting cost-effective technologies. By reducing data storage and operational costs, businesses can strategically allocate resources, thereby augmenting their overall financial sustainability. This trend aligns with the broader technological landscape, underscoring the significance of resource efficiency and cost-effectiveness across various industries, including data-centric sectors like IT, cloud computing, and data analytics.
RESTRAINTS:
Constraints associated with edge AI devices
At present, inference in edge AI predominantly relies on pre-trained machine learning (ML) models. These models adapt and fine-tune themselves according to users' data and specific requirements. The training process demands significant computational power, and edge AI is susceptible to uncertainties and randomness due to its restricted access to comprehensive training data. Additionally, while edge AI is proficient in handling modest transfer learning tasks, it faces limitations in undertaking more complex tasks that necessitate deep learning approaches.
OPPORTUNITIES:
The emergence of a 5G network to connect IT and telecom.
AI processors dedicated to on-device image analyses.
Artificial Intelligence (AI) processors specifically designed for on-device image analyses represent a noteworthy advancement as technology evolves. This trend signifies a focus on enhancing the processing capabilities of devices, particularly in the domain of image recognition and analysis. Tailored for handling complex visual tasks directly on the device without relying solely on cloud-based processing, these dedicated AI processors find integration into smartphones, cameras, and various smart devices. They empower real-time image recognition, interpretation, and analysis without the necessity for continuous connectivity to external servers, leading to improved speed and efficiency in handling image-related tasks. Notably, this approach also addresses privacy concerns by keeping sensitive data localized on the device. The emergence of these specialized AI processors aligns with a broader transition towards edge computing, emphasizing the execution of computational tasks closer to the source of data generation. This shift is particularly significant in applications like computer vision, augmented reality, and facial recognition, where swift and precise processing of visual information is paramount.
CHALLENGES:
Devices at the edge of artificial intelligence (AI), including smartphones, surveillance/security cameras, drones, and robots, possess on-device intelligence, allowing them to make decisions regarding the nearest point of interaction with the user. However, these edge AI devices encounter specific challenges concerning power consumption and device size. In contrast, cloud-based AI offers benefits such as simpler implementation, integration, and scalability, shifting the cost burden from capital to operational expenditure. Additionally, cloud-based AI provides the advantage of centrally storing data off-premises. The size of a neural network is directly linked to power demand, and an expansion in size corresponds to increased power consumption. The optimization of power consumption for deep neural network models through software poses a challenge, emphasizing the necessity to concentrate on the collaborative design of algorithms and hardware to achieve high-performance and power-efficient on-device AI.
As the world looks at the ongoing impact of the COVID-19 epidemic, the entire industry is affected. To reduce the risk of an epidemic, organizations around the world are taking adequate measures such as remote operation, remote storage and monitoring, plant automation, and telehealth. It has been shown to have a positive impact on vertical health care, as firms have begun to see the potential of Edge AI Hardware in combating the impact of COVID-19. This has led to an increase in funding and research to keep businesses safe and secure throughout the value chain. It is expected that the market will witness slower growth during the epidemic and return with a higher rate of acquisition in all direct post-epidemic situations. Organizations around the world have been using digital infrastructure to continue their normal business operations as they serve as an important infrastructure. Healthcare, public sector, and educational environments use digital at an unprecedented rate. Many cloud and cloud companies offer their free computer services to leading employees to minimize the impact of COVID-19.
In response to the ongoing crisis, notable strategic moves have been made, such as Qualcomm's acquisition of Cellwize, aimed at accelerating the deployment of connected intelligent edges through advanced 5G networks. This collaboration is crucial for expediting network deployment and enhancing network administration, serving as an adaptive measure in response to geopolitical tensions and their potential repercussions on market operations.
In terms of market geography and segmentation, North America has asserted itself as the largest region in the edge AI hardware market in 2023, with the Asia-Pacific region expected to experience the highest growth during the forecast period. The widespread use of edge AI hardware in devices like smartphones, cameras, robots, wearables, and smart speakers spans various industries, including consumer electronics, automotive, healthcare, and the industrial sector. The crisis has also led to technology sanctions on Russia, potentially impacting the 5G rollout and consequently affecting the edge AI hardware market, given 5G's critical role in enabling edge AI technologies. Supply chain disruptions resulting from sanctions may further influence the availability and cost of edge AI hardware components. Additionally, complications arising from the COVID-19 pandemic, affecting production facilities and causing increased demand for electronics and semiconductor products, have added intricacy to the market dynamics amid the Russia-Ukraine crisis. Driving factors in the market include the demand for mission-critical applications necessitating minimal latency and real-time data transmission, coupled with the emergence of 5G networks integrating IT and telecommunications. However, limitations associated with AI edge devices, including susceptibility to uncertainty and unpredictability due to limited access to training data, pose challenges.
The Edge AI Hardware Market is currently influenced by multiple factors, with susceptibility to economic downturns being a notable consideration. The market's growth is fueled by increased investments in AI startups, rising demands for smart homes and cities, and the imperative for automation and safety in organizational settings. While these factors suggest a flourishing market, the possibility of an economic slowdown poses a risk, potentially affecting investment levels and the pace of adopting new technologies, especially in smart homes, cities, and organizational automation. Consumer electronics are currently driving substantial demand, with the automotive and transportation sector expected to experience rapid growth, indicating a diverse application of Edge AI hardware. However, an economic downturn could lead to a differential impact on sectors, potentially reducing consumer spending in luxury or non-essential segments. Regionally, the Asia Pacific exhibits the highest projected Compound Annual Growth Rate (CAGR), driven by smartphone penetration and surveillance camera demand, while North America emphasizes reducing network latency and IoT device dependency. Economic factors in these regions may influence market dynamics, potentially slowing growth if consumer and organizational spending contracts. Despite these challenges, the market also offers opportunities that could help mitigate the impacts of an economic slowdown.
BY DEVICE
The edge AI hardware market is segmented into Smartphones, Surveillance cameras, Smart speakers, Edge servers, Robots, Wearables, Automotive and Smart mirrors. During the projection period the largest market share in edge AI hardware is held by the smartphones segment, primarily due to its early integration of AI chips or processors and advanced technologies in image recognition, real-time speech, and voice recognition, and the addition of AI and edge computing might improve their capabilities even more.
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BY END USER
The edge AI hardware market is segmented into Smart home, Automotive & Transportation, Industrial, Healthcare, Consumer electronics, Aerospace & defense, Government and Construction. The consumer electronics industry leads in terms of volume. This is due to rising consumer expenditure and demand for consumer electronics. Furthermore, the emergence of new use cases for edge AI might lead to rapid growth in the consumer electronics segment of the edge AI hardware industry.
North America is projected to lead in the largest market share for edge AI hardware during the forecast period. This dominance is attributed to the escalating demand for faster device processing, elevated latency caused by network congestion, the swift integration of edge AI in the United States and Canada, and a rising reliance on IoT devices. The utilization of AI in consumer electronics and smart home applications is a key driver for market growth in this region, supported by increasing government funding and a robust technical foundation. Meanwhile, the Asia-Pacific region is expected to witness the highest Compound Annual Growth Rate (CAGR) due to the presence of major semiconductor manufacturers and exporters, presenting an opportunity for significant market expansion.
The key players in the edge AI hardware are Google, Intel Corporation, MediaTek, NVIDIA Corporation, Samsung Electronics, Apple, Huawei Technologies, International Business Machines Corporation, Microsoft Corporation, Qualcomm Technologies & Other Players.
In November 2022: Lumen Technologies, a network solutions provider, initiated the expansion of its Edge Computing Solutions portfolio into the Asia-Pacific Region. This extension encompasses the introduction of its Edge Bare Metal pay-as-you-go hardware solution for servers, capitalizing on sites situated in Singapore and Japan.
In October 2022: Kneron secured a funding of USD 50 million for its next-gen AI hardware solutions. The company intends to utilize the funds to expedite its research and development efforts aimed at producing advanced AI inference modules. Kneron foresees a heightened adoption of on-device edge AI technology in the future, emphasizing the incorporation of AI computing power directly into hardware-equipped devices rather than relying on cloud software.
| Report Attributes | Details |
|---|---|
| Market Size in 2023 | US$ 1.27 Billion |
| Market Size by 2032 | US$ 6.90 Billion |
| CAGR | CAGR of 20.7% From 2024 to 2032 |
| Base Year | 2023 |
| Forecast Period | 2024-2032 |
| Historical Data | 2020-2022 |
| Report Scope & Coverage | Market Size, Segments Analysis, Competitive Landscape, Regional Analysis, DROC & SWOT Analysis, Forecast Outlook |
| Key Segments | • By Type (Programmable (FPGA & PLD) DSP IC, Application-Specific DSP IC, General-Purpose DSP IC) • By Component (Memory, Processor, Sensor, Other) • By Device (Smartphones, Surveillance Cameras, Smart Speakers, Edge Servers, Robots, Wearables, Automotive, Smart Mirrors) • By End User (Smart Home, Automotive & Transportation, Industrial, Healthcare, Consumer Electronics, Aerospace & Defense, Government, Construction) • By Function (Inference, Training) |
| Regional Analysis/Coverage | North America (US, Canada, Mexico), Europe (Eastern Europe [Poland, Romania, Hungary, Turkey, Rest of Eastern Europe] Western Europe] Germany, France, UK, Italy, Spain, Netherlands, Switzerland, Austria, Rest of Western Europe]), Asia Pacific (China, India, Japan, South Korea, Vietnam, Singapore, Australia, Rest of Asia Pacific), Middle East & Africa (Middle East [UAE, Egypt, Saudi Arabia, Qatar, Rest of Middle East], Africa [Nigeria, South Africa, Rest of Africa], Latin America (Brazil, Argentina, Colombia, Rest of Latin America) |
| Company Profiles | Google, Intel Corporation, MediaTek, NVIDIA Corporation, Samsung Electronics, Apple, Huawei Technologies, International Business Machines Corporation, Microsoft Corporation and Qualcomm Technologies. |
| Key Drivers | • The number of intelligent applications is rapidly increasing. • Data storage and operating costs are reduced. |
| Restraints | • Constraints associated with edge AI devices |
The Edge AI Hardware Market was valued at USD 1274.5 Million in 2023.
The expected CAGR of the Edge AI Hardware Market during the forecast period is 20.7%.
Edge AI devices, encompassing smartphones, surveillance/security cameras, drones, and robots, possess on-device intelligence, allowing them to make decisions regarding the nearest point of interaction with the user. Nevertheless, these devices encounter specific challenges concerning power consumption and device size.
Asia-Pacific is expected to exhibit the highest Compound Annual Growth Rate (CAGR) owing to the presence of leading manufacturers and exporters of semiconductor content, presenting an opportunity for market growth.
The North America region with the biggest market share in 2023.
TABLE OF CONTENTS
1. Introduction
1.1 Market Definition
1.2 Scope
1.3 Research Assumptions
2. Industry Flowchart
3. Research Methodology
4. Market Dynamics
4.1 Drivers
4.2 Restraints
4.3 Opportunities
4.4 Challenges
5. Impact Analysis
5.1 Impact of Russia-Ukraine Crisis
5.2 Impact of Economic Slowdown on Major Countries
5.2.1 Introduction
5.2.2 United States
5.2.3 Canada
5.2.4 Germany
5.2.5 France
5.2.6 UK
5.2.7 China
5.2.8 Japan
5.2.9 South Korea
5.2.9 India
6. Value Chain Analysis
7. Porter’s 5 Forces Model
8. Pest Analysis
9. Edge AI Hardware Market, By Component
9.1 Introduction
9.2 Trend Analysis
9.3 Memory
9.4 Processor
9.5 Sensor
9.6 Other
10. Edge AI Hardware Market, By Device
10.1 Introduction
10.2 Trend Analysis
10.3 Smartphones
10.4 Surveillance cameras
10.5 Smart speakers
10.6 Edge servers
10.7 Robots
10.8 Wearables
10.9 Automotive
10.10 Smart mirrors
11. Edge AI Hardware Market, By End-User
11.1 Introduction
11.2 Trend Analysis
11.3 Smart home
11.4 Automotive & transportation
11.5 Industrial
11.6 Healthcare
11.7 Consumer electronics
11.8 Aerospace & defense
11.9 Government
11.10 Construction
12. Edge AI Hardware Market, By Function
12.1 Introduction
12.2 Trend Analysis
12.3 Inference
12.4 Training
13. Regional Analysis
13.1 Introduction
14.2 North America
14.2.1 USA
14.2.2 Canada
14.2.3 Mexico
14.3 Europe
14.3.1 Eastern Europe
14.3.1.1 Poland
14.3.1.2 Romania
14.3.1.3 Hungary
14.3.1.4 Turkey
14.3.1.5 Rest of Eastern Europe
14.3.2 Western Europe
14.3.2.1 Germany
14.3.2.2 France
14.3.2.3 UK
14.3.2.4 Italy
14.3.2.5 Spain
14.3.2.6 Netherlands
14.3.2.7 Switzerland
14.3.2.8 Austria
14.3.2.9 Rest of Western Europe
14.4 Asia-Pacific
14.4.1 China
14.4.2 India
14.4.3 Japan
14.4.4 South Korea
14.4.5 Vietnam
14.4.6 Singapore
14.4.7 Australia
14.4.8 Rest of Asia Pacific
14.5 The Middle East & Africa
14.5.1 Middle East
14.5.1.1 UAE
14.5.1.2 Egypt
14.5.1.3 Saudi Arabia
14.5.1.4 Qatar
14.5.1.5 Rest of the Middle East
14.5.2 Africa
14.5.2.1 Nigeria
14.5.2.2 South Africa
14.5.2.3 Rest of Africa
14.6 Latin America
14.6.1 Brazil
14.6.2 Argentina
14.6.3 Colombia
14.6.4 Rest of Latin America
15. Company Profiles
15.1 Google
15.1.1 Company Overview
15.1.2 Financials
15.1.3 Products/ Services Offered
15.1.4 SWOT Analysis
15.1.5 The SNS View
15.2 Intel Corporation
15.2.1 Company Overview
15.2.2 Financials
15.2.3 Products/ Services Offered
15.2.4 SWOT Analysis
15.2.5 The SNS View
15.3 MediaTek
15.3.1 Company Overview
15.3.2 Financials
15.3.3 Products/ Services Offered
15.3.4 SWOT Analysis
15.3.5 The SNS View
15.4 NVIDIA Corporation
15.4 Company Overview
15.4.2 Financials
15.4.3 Products/ Services Offered
15.4.4 SWOT Analysis
15.4.5 The SNS View
15.5 Samsung Electronics
15.5.1 Company Overview
15.5.2 Financials
15.5.3 Products/ Services Offered
15.5.4 SWOT Analysis
15.5.5 The SNS View
15.6 Apple
15.6.1 Company Overview
15.6.2 Financials
15.6.3 Products/ Services Offered
15.6.4 SWOT Analysis
15.6.5 The SNS View
15.7 Huawei Technologies
15.7.1 Company Overview
15.7.2 Financials
15.7.3 Products/ Services Offered
15.7.4 SWOT Analysis
15.7.5 The SNS View
15.8 International Business Machines Corporation
15.8.1 Company Overview
15.8.2 Financials
15.8.3 Products/ Services Offered
15.8.4 SWOT Analysis
15.8.5 The SNS View
15.9 Microsoft Corporation
15.9.1 Company Overview
15.9.2 Financials
15.9.3 Products/ Services Offered
15.9.4 SWOT Analysis
15.9.5 The SNS View
15.10 Qualcomm Technologies
15.10.1 Company Overview
15.10.2 Financials
15.10.3 Products/ Services Offered
15.10.4 SWOT Analysis
15.10.5 The SNS View
16. Competitive Landscape
16.1 Competitive Benchmarking
16.2 Market Share Analysis
16.3 Recent Developments
16.3.1 Industry News
16.3.2 Company News
16.3.3 Mergers & Acquisitions
17. USE Cases and Best Practices
18. Conclusion
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BY COMPONENT
Memory
Processor
Sensor
Other
BY DEVICE
Smartphones
Surveillance cameras
Smart speakers
Edge servers
Robots
Wearables
Automotive
Smart mirrors
BY FUNCTION
Inference
Training
BY END USER
Smart home
Automotive & transportation
Industrial
Healthcare
Consumer electronics
Aerospace & defense
Government
Construction
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REGIONAL COVERAGE:
North America
Europe
Asia Pacific
Middle East & Africa
Latin America
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