Dublin, Nov. 15, 2022 (GLOBE NEWSWIRE) -- The "Global Edge AI Hardware Market by Device, Processor(CPU, GPU, and ASIC), Function, Power Consumption( Less than 1 W, 1-3 W, 3-5 W, 5-10 W, and more than 10 W), Vertical and Geography - Forecast to 2027" report has been added to ResearchAndMarkets.com's offering.
The edge AI hardware market is projected to grow from 1,056 Million Units to reach 2,716 million units by 2027; it is expected to grow at a CAGR of 20.8% during the forecast period. The major opportunities for the edge AI hardware market include the growing demand for IoT-based edge computing solutions and the rising adoption of 5G networks to bring IT and telecom together and dedicated AI processors for on-device image analytics. Major restraints for the market are limited on-device training and the shortage of AI experts. Designing efficient AI system pose major challenges to the edge AI hardware market.
Training to have second highest CAGR during the forecast period
Training is the process of developing an algorithm that will be used to infer the output. ML models are trained to develop the ability to understand a data set and act on new data. No actual learning happens on devices as training requires high computational power. As mobile devices do not have high-performance computing, ML models are trained on the cloud. Moreover, on-device training is not required for each application, and it will be limited to certain devices such as automotive systems and robots. Considering its advantages, on-device training is expected to increase in the next few years. With on-device training capability, a model can learn from a user's data available on the device, making the data more secure. With on-device training, an ML model can learn and update continuously.
US to grow with highest CAGR in North America during the forecast period
The US is the major revenue generator for players dealing in edge AI hardware in North America. The US is a key market for AI application processors as the demand for smartphones, smart home appliances, and advanced products such as IoT devices, wearable electronics, and vehicles with high-security features is high in the country. The US government has announced significant investments in machine learning solutions across various sectors, including consumer electronics, healthcare, and government. The abundance of capital and strong support of the US government contribute to the large-scale adoption of ML/AI solutions. The country's data center industry continues to grow with rising investments in artificial intelligence and technological advancements.
Market Dynamics
Drivers
- Ever-Increasing Enterprise Workloads on the Cloud
- Rapid Growth in the Number of Intelligent Applications
- Exponentially Growing Data Volume and Network Traffic
Restraints
- Privacy and Security Concerns Related to Edge AI Solutions
- Inadequate Number of AI Experts
Opportunities
- Emergence of the 5G Network to Bring IT and Telecom Together
- Advent of Autonomous Vehicles Coupled with Connected Car Infrastructure
- Rising Need of Edge Computing in IoT
Challenges
- Interoperability Issues Slowing the Adoption of Edge AI Software
- Optimization of Edge AI Standards
Key Topics Covered:
1 Introduction
2 Research Methodology
3 Executive Summary
4 Premium Insights
5 Market Overview and Industry Trends
6 Industry Trends
7 Edge AI Software Market, by Component
8 Edge AI Software Market, by Data Source
9 Edge AI Software Market, by Organization Size
10 Edge AI Software Market, by Vertical
11 Edge AI Software Market, by Region
12 Competitive Landscape
13 Company Profiles
14 Appendix
Companies Mentioned
- IBM
- Aws
- Anagog
- Veea
- Bragi
- Sixsq
- Kneron
- Gorilla Technology Group
- Deepbrainz
- Alefedge
- Stratahive
- Tact.AI
- Deci.AI
- Swim.AI
- Synaptics
- Bytelake
- Clearblade
- Horizon Robotics
- Foghorn Systems
- Imagimob
- Octonion
- Invision.AI
- Edgeworx
- Azion Technologies
- Nutanix
- Adapdix
- Reality AI
For more information about this report visit https://www.researchandmarkets.com/r/icnuu3
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