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邊緣AI加速器-新商機分析
市場調查報告書
商品編碼
1358191

邊緣AI加速器-新商機分析

Edge AI Accelerators-Emerging Opportunity Analysis
出版日期: | 出版商: Frost & Sullivan | 英文 53 Pages | 商品交期: 最快1-2個工作天內

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簡介目錄

擴大物聯網應用推動成長

對即時深度學習工作負載的需求不斷成長，使得專用邊緣人工智慧硬體對於實現快速設備上深度學習至關重要。此外，雲端基礎的人工智慧方法無法確保資料隱私、低延遲和高頻寬。因此，許多人工智慧工作負載正在轉移到邊緣，增加了對專門用於設備上機器學習推理的人工智慧硬體的需求。

物聯網的發展、消費性電子和汽車產業對智慧技術的採用以及智慧工業自動化正在推動邊緣人工智慧加速器市場的發展。適用於智慧型手機、穿戴式裝置和智慧家電等消費性應用的人工智慧加速器不僅需要小型化，還需要高處理成本比。同時，高處理速度和功效是許多工業和企業應用中使用的人工智慧加速器最重要的要求。

大多數晶片製造商都在努力提高處理速度，同時降低功耗。為了克服這個問題，公司正在投資開發用途晶片、高效能晶片架構、新演算法、先進記憶體和替代材料。為了利用這些技術進步，領先的公司正在採取聯盟和收購等技術策略。

預計美國、韓國、中國、日本、德國和以色列的邊緣人工智慧加速器市場將顯著成長。這是由於與家用電器、汽車、工業設備和國防相關的製造活動量很大。這些國家除了擁有強大的製造基礎外，還建立了強大的晶片製造生態系統，這對於維持市場主導地位至關重要。

深度學習、神經網路、電腦視覺、生成人工智慧和神經形態運算的出現正在為邊緣推理應用創造新的機會。隨著公司迅速轉向分散式電腦架構，他們正在學習應用該技術來提高生產力和降低成本的新方法。因此，人工智慧晶片開發人員需要更專注於開發旨在滿足這些使用案例特定要求的解決方案。

這份 Frost & Sullivan 研究報告涵蓋以下主題：

  • 主要人工智慧加速器技術概述和重要性
  • 主要邊緣AI處理器比較分析
  • 新使用案例
  • 產業企業技術趨勢及主要發展策略
  • AI加速器晶片產業商業模式
  • 邊緣AI加速器領域區域分析
  • AI加速器路線圖
  • 成長機會

目錄

戰略問題

  • 為什麼成長如此困難？策略要務 8 (TM)：阻礙成長的要素
  • The Strategic Imperative 8（TM）
  • 邊緣人工智慧加速器產業三大策略問題的影響
  • Growth Pipeline Engine（TM）：加速成長機會
  • 調查方法

成長機會分析

  • 分析範圍
  • 不同產業使用的邊緣AI加速器細分
  • 成長促進因素
  • 成長阻礙因素

新機會分析—邊緣AI加速器

  • 執行摘要
  • 關鍵硬體技術-CPU概述
  • 關鍵硬體技術-GPU概述
  • 關鍵硬體技術 - ASIC 概述
  • 主要邊緣AI CPU、GPU和ASIC對比分析
  • 按應用分析關鍵效能要素
  • 邊緣人工智慧加速器的新使用案例
  • 融合場景提高工業環境中的員工安全
  • 策略夥伴關係
  • 併購
  • 主要創新主題
  • 主要參與者和新產品開發配合措施
  • 針對新興企業和新產品開發的配合措施
  • AI加速器晶片產業商業模式
  • 邊緣人工智慧加速器生態系統
  • 邊緣人工智慧加速器的區域分析 - 亞太地區
  • 邊緣人工智慧加速器的區域分析 - 歐洲和以色列
  • 邊緣人工智慧加速器的區域分析 - 北美
  • AI加速器路線圖

成長機會宇宙

  • 成長機會 1：開發特定工作負載的 AI 加速器
  • 成長機會2：將AI晶片融入小型設備
  • 成長機會 3：開發更快的互連

附錄

  • 技術完備等級(TRL)：說明

下一步

  • 下一步
  • 為什麼是霜凍，為什麼是現在？
  • 免責聲明
簡介目錄
Product Code: DAB2

Expanding IoT Applications Drive Growth

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

Table of Contents

Strategic Imperatives

  • 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

Growth Opportunity Analysis

  • Scope of Analysis
  • Segmentation of Edge AI Accelerators Used In Different Industries
  • Growth Drivers
  • Growth Restraints

Emerging Opportunity Analysis-Edge AI Accelerators

  • 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
  • Analysis of Key Performance Factors for Different Applications
  • Emerging Use Cases of Edge AI Accelerators
  • 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

Growth Opportunity Universe

  • Growth Opportunity 1: Developing Workload-specific AI accelerators
  • Growth Opportunity 2: Including AI Chips in Smaller Devices
  • Growth Opportunity 3: Development of Faster Interconnects

Appendix

  • Technology Readiness Levels (TRL): Explanation

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