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市場調查報告書

邊緣AI的進步 - 新興應用與創新

Advancements in AI on Edge - Emerging Applications and Innovations

出版商 Frost & Sullivan 商品編碼 921048
出版日期 內容資訊 英文 44 Pages
商品交期: 最快1-2個工作天內
價格
邊緣AI的進步 - 新興應用與創新 Advancements in AI on Edge - Emerging Applications and Innovations
出版日期: 2019年12月26日內容資訊: 英文 44 Pages
簡介

傳統雲端運算模式從裝置發送用於數據解析的數據到雲端,並將其結果送回實施用的裝置。雲端運算具優異敏捷性,但仍不足以克服某些課題,如潛時、頻寬、即時決策的數據處理、雲端與邊緣之間的數據傳輸成本等。雲端AI模式經常需要使用從裝置蒐集而來的數據進行訓練,使得AI適用產生見解十分困難亦費時。邊緣運算的AI由於推論和訓練完全轉移至裝置,將能解決雲端所面臨的挑戰。

本報告研究邊緣AI,系統性彙整概要、既有模式課題、主要參與企業、未來創新應用、ROADMAP與里程碑等情報。

第1章 摘要整理

第2章 邊緣AI概要

  • 邊緣AI概要
  • 邊緣AI優點
  • 分散型AI改善操作上的即時性並降低隱私風險
  • 具體案例:邊緣的分散型AI

第3章 邊緣AI市場概要

  • AI推論工作負荷快速轉移至邊緣,推動了邊緣AI晶片組市場
  • 邊緣AI有助克服雲端運算相關課題

第4章 邊緣AI實施領域

  • 由於邊緣AI變革性影響減少Domain整體潛時,協助企業更快做出決策
  • 汽車參與企業正使用邊緣AI技術,開拓更高層級的自主性
  • 隨著邊緣AI的出現,實際店舖擁有推動線上購物的尖端工具
  • 供應鏈中的邊緣AI被使用於預估消費者需求與降低庫存成本
  • 事例1:商業管理之邊緣AI基礎的分析
  • 事例2:預測性維護之邊緣AI基礎的分析

第5章 矚目企業:提供邊緣AI技術的企業名單

  • 矚目企業 - 企業1:LGN.ai
  • 矚目企業 - 企業2:Horizon Robotics
  • 矚目企業 - 企業3:NVIDIA
  • 矚目企業 - 企業4:Intel
  • 矚目企業 - 企業5:IBM
  • 矚目企業 - 企業6:Qualcomm
  • 矚目企業 - 企業7:Google
  • 矚目企業 - 企業8:Imagimob
  • 矚目企業 - 企業9:Xnor.ai
  • 矚目企業 - 企業10:Gorilla Technology

第6章 夥伴與合作

  • 生態系統參與企業為加速邊緣AI採用進行合作
  • 創投業者正積極投資於邊緣方面提供AI功能的初創企業

第7章 未來ROADMAP

  • 邊緣運算取代雲端:商業展望
  • 邊緣運算是在資源限制環境中支援計算集約型AI應用的具前景解決方案

第8章 產業聯絡資訊

  • 主要聯絡資訊
  • 免責聲明
目錄
Product Code: D92E

An Insight Into How AI On Edge Is Likely To Open Up New Opportunities For Businesses In The Near Future

Traditional cloud computing models sends data from the device to the cloud for data analysis and the decision is sent back to the device for implementation. The agility of cloud computing is great but not enough to overcome certain challenges such as latency, bandwidth, processing the data for real-time decision making, costs associated with data transfer between cloud and edge. Cloud AI models often needed to be trained with data collected from devices, making it difficult and time consuming to apply AI and generate insights. AI with edge computing will solve the challenges faced in cloud, as the inference and training is totally moved towards the devices.

In brief, this research provides the following:

  • A brief snapshot of convergence of edge computing with AI
  • The challenges of existing cloud AI models and how edge can solve
  • Key participants delivering intelligent edge AI solutions for different industries
  • Highlights of innovative future applications through convergence models
  • Roadmap and key milestones to achieve in the near, medium and long term to make devices, machines and things more intelligent.

Table of Contents

1.0. Executive Summary

  • 1.1. Research Scope
  • 1.2. Research Methodology
  • 1.3. Research Methodology Explained
  • 1.4. Key Findings

2.0. Introduction to Edge AI

  • 2.1. Overview of AI on Edge
  • 2.2. Benefits of AI at the Edge
  • 2.3. Distributed AI Improves Operational Timeliness and Reduces Privacy Risks
  • 2.4. Specific Example: Distributed AI at the Edge

3.0. AI on Edge Market Overview

  • 3.1. Rapid Migration of AI Inference Workloads to the Edge is Driving the Edge AI Chipsets Market
  • 3.2. AI on Edge helps to Overcome the Challenges Associated with Cloud Computing

4.0. Areas of Edge AI Implementation

  • 4.1. The Transformative Impact of Edge AI Cuts down Latency across Domains, Helping Companies take Faster Decisions
  • 4.2. Automotive Participants are Making Efforts to Unlock Higher Levels of Autonomy using Edge AI Technology
  • 4.3. With the Advent of Edge AI, Brick and Mortar Stores Now have Advanced Tools to Stay Ahead against Online Shopping
  • 4.4. Edge AI in Supply Chains is Being Utilized to Predict Consumer Demand and Reduce Inventory Costs
  • 4.5. Case Example 1: Edge AI based Analytics for Business Management
  • 4.6. Case Example 2: Edge AI based Analytics for Predictive Maintenance

5.0. Companies to Watch: List of Companies Offering Edge AI Technology

  • 5.1. Companies to Watch - Company 1: LGN.ai
  • 5.2. Companies to Watch - Company 2: Horizon Robotics
  • 5.3. Companies to Watch - Company 3: NVIDIA
  • 5.4. Companies to Watch - Company 4: Intel
  • 5.5. Companies to Watch - Company 5: IBM
  • 5.6. Companies to Watch - Company 6: Qualcomm
  • 5.7. Companies to Watch - Company 7: Google
  • 5.8. Companies to Watch - Company 8: Imagimob
  • 5.9. Companies to Watch - Company 9: Xnor.ai
  • 5.10. Companies to Watch - Company 10: Gorilla Technology

6.0. Partnerships and Collaboration

  • 6.1. Participants in the Ecosystem are Partnering to Accelerate the Adoption of AI on the Edge
  • 6.2. Venture Capitalists are Investing Aggressively in Promising start-ups Offering AI capabilities at the Edge

7.0. Future Roadmap

  • 7.1. Will Edge Computing Replace Cloud: Business Perspective
  • 7.2. Edge Computing is a Promising Solution to Support Computation-intensive AI Applications in Resource Constrained Environments

8.0. Industry Contacts

  • 8.1. Key Contacts
  • Legal Disclaimer