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表紙：AIトレーニングチップの世界市場：2023年～2030年
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1382525

AIトレーニングチップの世界市場：2023年～2030年

Global AI Training Chip Market - 2023-2030
出版日: | 発行: DataM Intelligence | ページ情報: 英文 201 Pages | 納期: 約2営業日

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AIトレーニングチップの世界市場：2023年～2030年
出版日: 2023年11月17日
発行: DataM Intelligence
ページ情報: 英文 201 Pages
納期: 約2営業日
ご注意事項 :
本レポートは最新情報反映のため適宜更新し、内容構成変更を行う場合があります。ご検討の際はお問い合わせください。
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  • 概要
  • 目次
概要

概要

世界のAIトレーニングチップ市場は、2022年に153億米ドルに達し、2023年～2030年の予測期間中にCAGR29.2%で成長し、2030年には1,327億米ドルに達すると予測されます。

世界のAIトレーニングチップ市場は、幅広い産業でAIを活用したアプリケーションやサービスの需要が高まっていることから急成長しています。AIチップは、AIモデルの訓練と推論を加速するように設計された特殊な集積回路です。通常、データセンターやその他の高性能コンピューティング環境で使用されます。

AIトレーニングチップ市場は、幅広い産業や用途で有用性を発揮します。物体の検出、センサーデータの組み合わせ、自律走行車の分野での判断などの業務を推進し、安全性を高め、自動運転機能を実現します。AIチップはヘルスケアにおいて、医療写真の評価やX線、MRI、CTスキャンからの診断の補助に役立ちます。AIチップは、音声認識や言語翻訳といった言語関連のAIタスクを提供し、バーチャルアシスタントや即時言語翻訳ツールの進歩につながります。

CPUチップタイプが最も高い市場シェアを占めています。同様に、アジア太平洋地域はAIトレーニングチップ市場を独占しており、55%以上の最大市場シェアを獲得しています。同地域はAIトレーニングチップの開発と製造の主要拠点となっています。中国がアジア太平洋のAIトレーニングチップ市場全体の60%以上の最大シェアを占め、日本、韓国がこれに続きます。

ダイナミクス

深層学習アルゴリズムの人気の高まり

ディープラーニングアルゴリズムは、人工ニューラルネットワークを使用してデータから学習する機械学習アルゴリズムの一種です。画像認識、自然言語処理、音声認識など、さまざまな用途で利用されています。ディープラーニングアルゴリズムは非常に計算量が多いため、学習には多くの処理能力が必要となります。そこで、AIトレーニングチップの出番となります。AIトレーニングチップは、ディープラーニングアルゴリズムのトレーニングを加速するために特別に設計されています。通常、多数のコアと高性能メモリを搭載しており、大量のデータを迅速かつ効率的に処理できます。

ディープラーニングアルゴリズムの人気の高まりが、AIトレーニングチップの需要を牽引しています。市場の開拓は、さまざまな業界でディープラーニング技術の採用が進み、より強力で効率的な新しいAIトレーニングチップが開発されることによって推進されます。ディープラーニング技術を採用する企業や組織が増えるにつれ、AIトレーニングチップの需要は今後も伸び続けると予想されます。

幅広い業界におけるAI搭載アプリケーションの需要増加

AIを活用したアプリケーションは、ヘルスケア、製造、自動車、小売、金融など、さまざまな業界で利用されています。ヘルスケア分野では、新薬の開発、病気の診断、個人に合わせた治療計画の提供にAIが活用されています。さらに自動車分野では、自動運転車の開発、交通管理の改善、パーソナライズされた運転体験の提供などにAIが活用されています。

AIを活用したアプリケーションの開発と展開には、多くのコンピューティングパワーが必要となります。そこで登場するのがAIトレーニングチップです。AIトレーニングチップは、AIモデルのトレーニングを加速するために特別に設計されています。通常、多数のコアと高性能メモリを搭載しており、大量のデータを迅速かつ効率的に処理できます。AI技術を採用する企業や組織が増えるにつれ、AIトレーニングチップの需要は今後も伸び続けると予想されます。

熟練労働者の不足

AIトレーニングチップの開発と展開には、熟練した労働力が必要です。しかし、半導体業界では熟練労働者が不足しています。これは、半導体産業が高度に専門化された分野であり、多くの訓練と経験を必要とするためです。

熟練労働者の不足は、様々な形でAIトレーニングチップ市場の成長を抑制しています。第一に、企業が新しいAIアプリケーションを開発・展開することが難しくなっています。第二に、AIアプリケーションの開発・導入コストが増大しています。第三に、AIトレーニングチップ市場の技術革新のペースを遅らせています。

多くの国々は、熟練労働者の不足に対処するため、外国人人材の誘致を模索しています。それは、魅力的なビザや移民政策を提供したり、財政的なインセンティブを提供したりすることで可能です。熟練労働者の不足に対処することで、AIトレーニングチップ市場は成長を続け、新しいAIアプリケーションの開拓を支援することができます。

目次

第1章 調査手法と調査範囲

第2章 定義と概要

第3章 エグゼクティブサマリー

第4章 市場力学

  • 影響要因
    • 促進要因
      • ディープラーニングアルゴリズムの人気の高まり
      • 幅広い業界におけるAIを活用したアプリケーションの需要の増加
    • 抑制要因
      • 熟練労働者の不足
    • 機会
    • 影響分析

第5章 産業分析

  • ポーターのファイブフォース分析
  • サプライチェーン分析
  • 価格分析
  • 規制分析
  • ロシア・ウクライナ戦争の影響分析
  • DMI意見

第6章 COVID-19分析

第7章 ハードウェア別

  • プロセッサー
  • メモリ
  • ネットワーク
  • その他

第8章 チップタイプ別

  • GPU
  • CPU
  • ASIC
  • FPGA
  • その他

第9章 テクノロジー別

  • システムオンチップ
  • システム イン パッケージ
  • マルチチップモジュール
  • その他

第10章 用途別

  • 自然言語処理
  • ロボット工学
  • コンピュータビジョン
  • ネットワークセキュリティ
  • その他

第11章 エンドユーザー別

  • BFSI
  • ヘルスケア
  • 自動車・運輸
  • IT・通信
  • その他

第12章 地域別

  • 北米
    • 米国
    • カナダ
    • メキシコ
  • 欧州
    • ドイツ
    • 英国
    • フランス
    • イタリア
    • ロシア
    • その他欧州
  • 南米
    • ブラジル
    • アルゼンチン
    • その他南米
  • アジア太平洋地域
    • 中国
    • インド
    • 日本
    • オーストラリア
    • その他アジア太平洋地域
  • 中東・アフリカ

第13章 競合情勢

  • 競合シナリオ
  • 市況/シェア分析
  • M&A分析

第14章 企業プロファイル

  • Tesla, Inc.
    • 会社概要
    • 製品ポートフォリオと説明
    • 財務概要
    • 主な発展
  • NVIDIA Corporation
  • Intel Corporation
  • Graphcore Limited
  • Google Corporation
  • Qualcomm Technologies, Inc.
  • Shanghai Enflame Technology Co Ltd
  • Kunlun Core（Beijing）Technology Co., Ltd.
  • T-Head（Hangzhou）Semiconductor Co., Ltd.
  • MetaX Integrated Circuits（Shanghai）Co., Ltd.

第15章 付録

目次
Product Code: ICT7439

Overview

Global AI Training Chip Market reached US$ 15.3 billion in 2022 and is expected to reach US$ 132.7 billion by 2030, growing with a CAGR of 29.2% during the forecast period 2023-2030.

The global AI training chip market is growing rapidly due to the increasing demand for AI-powered applications and services across a wide range of industries. AI chips are specialized integrated circuits that are designed to accelerate the training and inference of AI models. It is typically used in data centers and other high-performance computing environments.

The AI training chip market provides usefulness in a wide range of industries and applications. It drives duties like as detecting objects, combining sensor data and making judgments in the area of autonomous vehicles, hence enhancing safety and enabling self-driving capabilities. AI chips are useful in healthcare for evaluating medical pictures and aiding diagnosis from X-rays, MRIs and CT scans. AI chips provide language-related AI tasks such as speech recognition and language translation, leading to advancements in virtual assistants and instantaneous language translation tools.

The CPU chip type accounts for the highest market share. Similarly, the Asia-Pacific dominates the AI training chip market, capturing the largest market share of over 55%. The region has been a major hub for the development and manufacturing of AI training chips. China accounted for the largest share of over 60% of the total AI training chip market in Asia-Pacific, followed by Japan and South Korea.

Dynamics

Growing popularity of deep learning algorithms

Deep learning algorithms are a type of machine learning algorithm that uses artificial neural networks to learn from data. It is used in a wide variety of applications, such as image recognition, natural language processing and speech recognition. Deep learning algorithms are very computationally intensive, which means that they require a lot of processing power to train. The is where AI training chips come in. AI training chips are specifically designed to accelerate the training of deep learning algorithms. It is typically equipped with a large number of cores and high-performance memory, which allows them to process large amounts of data quickly and efficiently.

The growing popularity of deep learning algorithms is driving the demand for AI training chips. The growth of the market will be driven by the increasing adoption of deep learning technologies in various industries and the development of new AI training chips that are more powerful and efficient. As more and more businesses and organizations adopt deep learning technologies, the demand for AI training chips is expected to continue to grow.

Increasing demand for AI-powered applications in a wide range of industries

AI-powered applications are being used in a variety of industries, including healthcare, manufacturing, automotive, retail and finance. In the healthcare sector, AI is being used to develop new drugs, diagnose diseases and provide personalized treatment plans. Furthermore, in the automotive sector, AI is being used to develop self-driving cars, improve traffic management and personalized driving experiences.

The development and deployment of AI-powered applications require a lot of computing power. The is where AI training chips come in. AI training chips are specifically designed to accelerate the training of AI models. It is typically equipped with a large number of cores and high-performance memory, which allows them to process large amounts of data quickly and efficiently. As more and more businesses and organizations adopt AI technologies, the demand for AI training chips is expected to continue to grow.

Shortage of skilled labor workforce

The development and deployment of AI training chips require a skilled workforce. However, there is a shortage of skilled workers in the semiconductor industry. The is due to the fact that the semiconductor industry is a highly specialized field and requires a lot of training and experience.

The shortage of skilled labor is restraining the growth of the AI training chip market in a number of ways. First, it is making it more difficult for companies to develop and deploy new AI applications. Second, it is increasing the cost of developing and deploying AI applications. Third, it is slowing down the pace of innovation in the AI training chip market.

Many countries are looking to attract foreign talent to help address the shortage of skilled workers. It can be done by offering attractive visa and immigration policies, as well as by providing financial incentives. By addressing the shortage of skilled labor, the AI training chip market can continue to grow and support the development of new AI applications.

Segment Analysis

The global AI training chip market is segmented based on hardware, chip type, technology, application, end-user and region.

Inexpensive, Easy to find and well-supported by Software Developers

CPUs are general-purpose processors that are designed to perform a variety of tasks. However, they are not specifically designed for AI applications. Despite this, CPUs are becoming increasingly popular for AI training because they are relatively inexpensive and easy to find. It is also well-supported by software developers.

CPUs are relatively inexpensive compared to other types of AI training chips, such as GPUs and ASICs. The makes them a good option for businesses and organizations that are on a budget. It is readily available from a variety of vendors. The makes it easy for businesses and organizations to get their hands on the chips they need. There are a wide variety of software tools available for developing and deploying AI applications on CPUs. The makes it easy for businesses and organizations to get started with AI training.

Geographical Penetration

Growing number of startups and continuous government support

Asia-Pacific has been a dominant force in the global AI training chip market. The region is home to some of the leading players in the AI training chip market, such as Intel, NVIDIA and Qualcomm. Asia-Pacific is a major hub for the adoption of AI technologies. The region is home to some of the world's largest economies, such as China, India and Japan. The economies are investing heavily in AI technologies to improve their competitiveness.

Asia-Pacific is home to a growing number of startups that are developing AI applications. The startups are driving the demand for AI training chips. For example, MediaTek is a Taiwanese multinational semiconductor company that offers a range of AI training chips. The company's AI training chips are used in a variety of applications, including smartphones and tablets. The region has a large pool of skilled labor in the semiconductor industry. The makes it a good place to develop and manufacture AI training chips. Governments in Asia-Pacific are supporting the development of AI technologies. The is helping to create a favorable environment for the growth of the AI training chip market.

COVID-19 Impact Analysis

The COVID-19 pandemic has had a mixed impact on the AI training chip market. On the one hand, the pandemic has led to an increase in demand for AI training chips, as businesses and organizations have turned to AI to automate tasks and improve efficiency. On the other hand, the pandemic has also caused disruptions to the supply chain, making it more difficult to obtain AI training chips.

The pandemic has led to an increased demand for AI training chips, as businesses and organizations have turned to AI to automate tasks and improve efficiency. The is because AI can be used to perform tasks such as facial recognition, contact tracing and fraud detection, which are all important in the fight against the COVID-19 outbreak. The pandemic has accelerated innovation in the AI training chip market. Chipmakers are developing new AI training chips that are more powerful and efficient. The is because businesses and organizations are willing to pay more for chips that can help them automate tasks and improve efficiency.

Russia-Ukraine War Impact Analysis

The Russia-Ukraine war is having a significant impact on the AI training chip market. The war has disrupted the supply chain for AI training chips, as many of the components used to make these chips are manufactured in Russia and Ukraine. The has led to shortages and price increases for AI training chips. The shortages of AI training chips have led to price increases. The is making it more expensive for businesses and organizations to develop and deploy AI applications.

In addition, the war has increased uncertainty in the global economy, which is making businesses and organizations hesitant to invest in new AI projects. The is also having a negative impact on the demand for AI training chips. The war is also delaying the development of new AI training chips. The is because many of the companies that are developing these chips have operations in Russia and Ukraine.

Businesses and organizations should work with their suppliers to develop contingency plans in case of further disruptions. The Russia-Ukraine war is a major challenge for the AI training chip market. However, by taking steps to mitigate the impact of the war, businesses and organizations can continue to develop and deploy AI applications.

By Hardware

  • Processor
  • Memory
  • Network
  • Others

By Chip Type

  • GPU
  • CPU
  • ASIC
  • FPGA
  • Others

By Technology

  • System on Chip
  • System in Package
  • Multi-chip Module
  • Others

By Application

  • Natural Language Processing
  • Robotics
  • Computer Vision
  • Network Security
  • Others

By End-User

  • BFSI
  • Healthcare
  • Automotive and Transportation
  • IT and Telecommunications
  • Others

By Region

  • North America
    • U.S.
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • France
    • Italy
    • Russia
    • Rest of Europe
  • South America
    • Brazil
    • Argentina
    • Rest of South America
  • Asia-Pacific
    • China
    • India
    • Japan
    • Australia
    • Rest of Asia-Pacific
  • Middle East and Africa

Key Developments

  • On July 2o, 2023, Tesla starts production of Dojo supercomputer to train driverless cars. It uses Tesla-designed chips and the entire infrastructure, as well as video data from the Tesla fleet, to train the neural network that is critical to supporting Tesla's machine vision technology for autonomous driving.
  • On May 28, 2023, NVIDIA announced a new class of large-memory AI supercomputer - an NVIDIA DGX supercomputer powered by NVIDIA GH200 Grace Hopper Superchips and the NVIDIA NVLink Switch System - created to enable the development of giant, next-generation models for generative AI language applications, recommender systems and data analytics workloads.
  • On August 30, 2023, Google made its artificial intelligence-powered tools available to enterprise customers at a monthly price of US$30 per user. Google's new tools include "Duet AI in Workspace", which will assist customers across its apps with writing in Docs, drafting emails in Gmail and generating custom visuals in Slides, among others.

Competitive Landscape

major global players in the market include: Tesla, Inc., NVIDIA Corporation, Intel Corporation, Graphcore Limited, Google Corporation, Qualcomm Technologies, Inc., Shanghai Enflame Technology Co Ltd, Kunlun Core (Beijing) Technology Co., Ltd., T-Head (Hangzhou) Semiconductor Co., Ltd. and MetaX Integrated Circuits (Shanghai) Co., Ltd.

Why Purchase the Report?

  • To visualize the global AI training chip market segmentation based on hardware, chip type, technology, application, end-user and region, as well as understand key commercial assets and players.
  • Identify commercial opportunities by analyzing trends and co-development.
  • Excel data sheet with numerous data points of AI training chip market-level with all segments.
  • PDF report consists of a comprehensive analysis after exhaustive qualitative interviews and an in-depth study.
  • Product mapping available as excel consisting of key products of all the major players.

The global AI training chip market report would provide approximately 77 tables, 85 figures and 201 Pages.

Target Audience 2023

  • Manufacturers/ Buyers
  • Industry Investors/Investment Bankers
  • Research Professionals
  • Emerging Companies

Table of Contents

1. Methodology and Scope

  • 1.1. Research Methodology
  • 1.2. Research Objective and Scope of the Report

2. Definition and Overview

3. Executive Summary

  • 3.1. Snippet by Hardware
  • 3.2. Snippet by Chip Type
  • 3.3. Snippet by Technology
  • 3.4. Snippet by Application
  • 3.5. Snippet by End-User
  • 3.6. Snippet by Region

4. Dynamics

  • 4.1. Impacting Factors
    • 4.1.1. Drivers
      • 4.1.1.1. Growing Popularity of Deep Learning Algorithms
      • 4.1.1.2. Increasing Demand for AI-powered Applications in a Wide Range of Industries
    • 4.1.2. Restraints
      • 4.1.2.1. Shortage of Skilled Labor Workforce
    • 4.1.3. Opportunity
    • 4.1.4. Impact Analysis

5. Industry Analysis

  • 5.1. Porter's Five Force Analysis
  • 5.2. Supply Chain Analysis
  • 5.3. Pricing Analysis
  • 5.4. Regulatory Analysis
  • 5.5. Russia-Ukraine War Impact Analysis
  • 5.6. DMI Opinion

6. COVID-19 Analysis

  • 6.1. Analysis of COVID-19
    • 6.1.1. Scenario Before COVID
    • 6.1.2. Scenario During COVID
    • 6.1.3. Scenario Post COVID
  • 6.2. Pricing Dynamics Amid COVID-19
  • 6.3. Demand-Supply Spectrum
  • 6.4. Government Initiatives Related to the Market During Pandemic
  • 6.5. Manufacturers Strategic Initiatives
  • 6.6. Conclusion

7. By Hardware

  • 7.1. Introduction
    • 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Hardware
    • 7.1.2. Market Attractiveness Index, By Hardware
  • 7.2. Processor*
    • 7.2.1. Introduction
    • 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 7.3. Memory
  • 7.4. Network
  • 7.5. Others

8. By Chip Type

  • 8.1. Introduction
    • 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Chip Type
    • 8.1.2. Market Attractiveness Index, By Chip Type
  • 8.2. GPU*
    • 8.2.1. Introduction
    • 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 8.3. CPU
  • 8.4. ASIC
  • 8.5. FPGA
  • 8.6. Others

9. By Technology

  • 9.1. Introduction
    • 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 9.1.2. Market Attractiveness Index, By Technology
  • 9.2. System on Chip*
    • 9.2.1. Introduction
    • 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 9.3. System in Package
  • 9.4. Multi-chip Module
  • 9.5. Others

10. By Application

  • 10.1. Introduction
    • 10.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 10.1.2. Market Attractiveness Index, By Application
  • 10.2. Natural Language Processing*
    • 10.2.1. Introduction
    • 10.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 10.3. Robotics
  • 10.4. Computer Vision
  • 10.5. Network Security
  • 10.6. Others

11. By End-User

  • 11.1. Introduction
    • 11.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 11.1.2. Market Attractiveness Index, By End-User
  • 11.2. BFSI*
    • 11.2.1. Introduction
    • 11.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 11.3. Healthcare
  • 11.4. Automotive and Transportation
  • 11.5. IT and Telecommunications
  • 11.6. Others

12. By Region

  • 12.1. Introduction
    • 12.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
    • 12.1.2. Market Attractiveness Index, By Region
  • 12.2. North America
    • 12.2.1. Introduction
    • 12.2.2. Key Region-Specific Dynamics
    • 12.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Hardware
    • 12.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Chip Type
    • 12.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 12.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 12.2.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 12.2.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 12.2.8.1. U.S.
      • 12.2.8.2. Canada
      • 12.2.8.3. Mexico
  • 12.3. Europe
    • 12.3.1. Introduction
    • 12.3.2. Key Region-Specific Dynamics
    • 12.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Hardware
    • 12.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Chip Type
    • 12.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 12.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 12.3.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 12.3.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 12.3.8.1. Germany
      • 12.3.8.2. UK
      • 12.3.8.3. France
      • 12.3.8.4. Italy
      • 12.3.8.5. Russia
      • 12.3.8.6. Rest of Europe
  • 12.4. South America
    • 12.4.1. Introduction
    • 12.4.2. Key Region-Specific Dynamics
    • 12.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Hardware
    • 12.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Chip Type
    • 12.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 12.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 12.4.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 12.4.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 12.4.8.1. Brazil
      • 12.4.8.2. Argentina
      • 12.4.8.3. Rest of South America
  • 12.5. Asia-Pacific
    • 12.5.1. Introduction
    • 12.5.2. Key Region-Specific Dynamics
    • 12.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Hardware
    • 12.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Chip Type
    • 12.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 12.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 12.5.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 12.5.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 12.5.8.1. China
      • 12.5.8.2. India
      • 12.5.8.3. Japan
      • 12.5.8.4. Australia
      • 12.5.8.5. Rest of Asia-Pacific
  • 12.6. Middle East and Africa
    • 12.6.1. Introduction
    • 12.6.2. Key Region-Specific Dynamics
    • 12.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Hardware
    • 12.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Chip Type
    • 12.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 12.6.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 12.6.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User

13. Competitive Landscape

  • 13.1. Competitive Scenario
  • 13.2. Market Positioning/Share Analysis
  • 13.3. Mergers and Acquisitions Analysis

14. Company Profiles

  • 14.1. Tesla, Inc.*
    • 14.1.1. Company Overview
    • 14.1.2. Product Portfolio and Description
    • 14.1.3. Financial Overview
    • 14.1.4. Key Developments
  • 14.2. NVIDIA Corporation
  • 14.3. Intel Corporation
  • 14.4. Graphcore Limited
  • 14.5. Google Corporation
  • 14.6. Qualcomm Technologies, Inc.
  • 14.7. Shanghai Enflame Technology Co Ltd
  • 14.8. Kunlun Core (Beijing) Technology Co., Ltd.
  • 14.9. T-Head (Hangzhou) Semiconductor Co., Ltd.
  • 14.10. MetaX Integrated Circuits (Shanghai) Co., Ltd.

LIST NOT EXHAUSTIVE

15. Appendix

  • 15.1. About Us and Services
  • 15.2. Contact Us