Edge AI Accelerators—Emerging Opportunity Analysis

Edge AI Accelerators—Emerging Opportunity Analysis

Expanding IoT Applications Drive Growth

RELEASE DATE
20-Sep-2023
REGION
Global
Deliverable Type
Technology Research
Research Code: DAB2-01-00-00-00
SKU: ES_2023_151
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Description

Specialized edge AI hardware that enables quick deep learning on-device has become essential due to the rising need for real-time deep learning workloads. Additionally, a cloud-based AI method cannot ensure data privacy, low latency, or offer high bandwidth. As a result, many AI workloads are shifting to the edge, increasing the demand for specialized AI hardware for on-device machine learning inference.

The growth of IoT, smart technology adoption by consumer electronics and the automotive industry, and intelligent industrial automation are propelling the edge AI accelerator market. AI accelerators in consumer-oriented applications, such as smartphones, wearables, and smart appliances, need to have a high processing-to-cost ratio as well as a smaller size. On the other hand, for most of the AI accelerators used in industrial/enterprise applications, the requirement for high processing speed and power efficiency are of prime significance.

The majority of chip manufacturers are struggling to improve processing speed while reducing power consumption. To overcome this, organizations are investing in developing application-specific chips, efficient chip architectures, new algorithms, advanced memories, and alternative materials. To leverage these technological advancements, major corporations are embracing technology strategies such as partnerships and acquisitions.

The market for edge AI accelerators is projected to grow significantly in the United States, South Korea, China, Japan, Germany, and Israel. This is due to the high amount of manufacturing activity pertaining to consumer electronics, automotive, industrial equipment, and defense. Apart from having a strong manufacturing base, these countries have also developed a strong ecosystem for chip manufacturing, which is crucial to maintaining a dominant position in the market.

The emergence of deep learning, neural networks, computer vision, generative artificial intelligence, and neuromorphic computing has created new opportunities for edge inferencing applications. While enterprises are quickly moving towards a decentralized computer architecture, they are also learning new methods to apply this technology to boost productivity and cut costs. Therefore, AI chip developers should focus more on developing solutions that are designed to fulfill these requirements specific to use cases.

This Frost & Sullivan research report covers the following topics:

•     Overview and significance of key AI accelerator technologies
•     Comparative analysis of key edge AI processors
•     Emerging use cases
•     Technology trends and key developmental strategies used by players in the industry
•     Business models in the AI accelerator chip industry
•     Regional analysis of the edge AI accelerator space
•     AI accelerators roadmap
•     Growth opportunities

RESEARCH: INFOGRAPHIC

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Table of Contents

Why Is It Increasingly Difficult to Grow?The Strategic Imperative 8™: Factors Creating Pressure on Growth

The Strategic Imperative 8™

The Impact of the Top 3 Strategic Imperatives of Edge AI Accelerators Industry

Growth Opportunities Fuel the Growth Pipeline Engine™

Research Methodology

Scope of Analysis

Segmentation of Edge AI Accelerators Used In Different Industries

Growth Drivers

Growth Restraints

Executive Summary

Key Hardware Technologies—CPU Overview

Key Hardware Technologies—GPU Overview

Key Hardware Technologies—ASIC Overview

Comparative Analysis of Key Edge AI CPUs, GPUs, and ASICs

Comparative Analysis of Key Edge AI CPUs, GPUs, and ASICs (continued)

Analysis of Key Performance Factors for Different Applications

Emerging Use Cases of Edge AI Accelerators

Emerging Use Cases of Edge AI Accelerators (continued)

Emerging Use Cases of Edge AI Accelerators (continued)

Convergence Scenario: Enhancing Employee Safety in Industrial Environments

Strategic Partnerships

Mergers and Acquisitions

Key Innovation Themes

Key Players and New Product Development Initiatives

Start-ups and New Product Development Initiatives

Business Models in the AI Accelerator Chip Industry

Ecosystem of Edge AI Accelerators

Regional Analysis of Edge AI Accelerator—APAC

Regional Analysis of Edge AI Accelerator—Europe and Israel

Regional Analysis of Edge AI Accelerator—North America

AI Accelerators Roadmap

AI Accelerators Roadmap (continued)

AI Accelerators Roadmap (continued)

Growth Opportunity 1: Developing Workload-specific AI accelerators

Growth Opportunity 1: Developing Workload-specific AI accelerators (continued)

Growth Opportunity 2: Including AI Chips in Smaller Devices

Growth Opportunity 2: Including AI Chips in Smaller Devices (continued)

Growth Opportunity 3: Development of Faster Interconnects

Growth Opportunity 3: Development of Faster Interconnects (continued)

Technology Readiness Levels (TRL): Explanation

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Specialized edge AI hardware that enables quick deep learning on-device has become essential due to the rising need for real-time deep learning workloads. Additionally, a cloud-based AI method cannot ensure data privacy, low latency, or offer high bandwidth. As a result, many AI workloads are shifting to the edge, increasing the demand for specialized AI hardware for on-device machine learning inference. The growth of IoT, smart technology adoption by consumer electronics and the automotive industry, and intelligent industrial automation are propelling the edge AI accelerator market. AI accelerators in consumer-oriented applications, such as smartphones, wearables, and smart appliances, need to have a high processing-to-cost ratio as well as a smaller size. On the other hand, for most of the AI accelerators used in industrial/enterprise applications, the requirement for high processing speed and power efficiency are of prime significance. The majority of chip manufacturers are struggling to improve processing speed while reducing power consumption. To overcome this, organizations are investing in developing application-specific chips, efficient chip architectures, new algorithms, advanced memories, and alternative materials. To leverage these technological advancements, major corporations are embracing technology strategies such as partnerships and acquisitions. The market for edge AI accelerators is projected to grow significantly in the United States, South Korea, China, Japan, Germany, and Israel. This is due to the high amount of manufacturing activity pertaining to consumer electronics, automotive, industrial equipment, and defense. Apart from having a strong manufacturing base, these countries have also developed a strong ecosystem for chip manufacturing, which is crucial to maintaining a dominant position in the market. The emergence of deep learning, neural networks, computer vision, generative artificial intelligence, and neuromorphic computing has created new opportunities for edge inferencing applications. While enterprises are quickly moving towards a decentralized computer architecture, they are also learning new methods to apply this technology to boost productivity and cut costs. Therefore, AI chip developers should focus more on developing solutions that are designed to fulfill these requirements specific to use cases. This Frost & Sullivan research report covers the following topics: • Overview and significance of key AI accelerator technologies • Comparative analysis of key edge AI processors • Emerging use cases • Technology trends and key developmental strategies used by players in the industry • Business models in the AI accelerator chip industry • Regional analysis of the edge AI accelerator space • AI accelerators roadmap • Growth opportunities
More Information
Deliverable Type Technology Research
Author Himanshu Kashinath Mhatre
Industries Electronics and Sensors
No Index No
Is Prebook No
Keyword 1 Edge Ai Accelerators
Keyword 2 Edge Computing In Ai
Keyword 3 Ai Accelerator Industry Insights
Podcast No
WIP Number DAB2-01-00-00-00