ディープラーニングチップセット市場：タイプ別、エンドユーザー別-2025-2030年の世界予測

ディープラーニングチップセット市場の2023年の市場規模は102億3,000万米ドル、2024年には118億2,000万米ドルに達すると予測され、CAGR 15.69％で成長し、2030年には283億9,000万米ドルに達すると予測されています。

ディープラーニングチップセット市場は、人工知能とハードウェア工学のダイナミックな交差を包含し、計算効率、速度、能力の進歩を促進します。GPU、TPU、ニューロモルフィックチップ、FPGAを含むこれらのチップセットは、リアルタイムのデータ処理、複雑な問題解決、自動化を可能にすることで、自動車、ヘルスケア、金融、家電などの多様な業界のアプリケーションに力を与えています。ディープラーニング・チップセットの必要性は、意思決定、予測分析、業務効率を高めるAI主導型ソリューションに対する需要の高まりによって強調されています。最終用途の範囲は広く、自律走行システム、医療画像診断、個別金融サービス、スマートデバイスに及び、IoT技術の普及と機械学習アルゴリズムの進歩によって強化されています。主な成長要因の調査から、エンドユーザー産業におけるAI導入の増加、AI研究開発への投資の拡大、スマートインフラの台頭が極めて重要な影響を及ぼしていることが明らかになった。新たなビジネスチャンスは、エネルギー効率と高速化のためのチップアーキテクチャの最適化や、処理能力の飛躍的な向上を約束するディープラーニングと量子コンピューティングの相互作用の拡大にあります。しかし、市場は高い開発コスト、技術的な複雑さ、高度なAIワークロードをサポートするためのスケーラブルなインフラの必要性などの課題に直面しています。さらに、データのプライバシーやセキュリティに対する懸念もあり、市場の信頼性に影響を与える可能性があります。技術革新の重要な分野には、特定のAIアプリケーションのためのチップ設計の改善、ニューラルネットワークのトレーニング効果の強化、ニッチ市場と差別化の可能性を提供するエッジデバイスへのディープラーニング機能の組み込みなどが含まれます。事業成長のためには、企業は協業パートナーシップを活用した機敏な戦略に注力し、最先端のチップ設計とAI展開のためのスキル開発に投資すべきです。競合情勢でありながら有望な市場であり、AI革命の技術的限界と高まる要求の両方に対応する先駆的ソリューションの機が熟しています。

市場力学：急速に進化するディープラーニングチップセット市場の主要市場インサイトを公開

ディープラーニングチップセット市場は、需要と供給のダイナミックな相互作用によって変貌を遂げています。このような市場力学の進化を理解することで、企業は十分な情報に基づいた投資決定、戦略的決定の精緻化、そして新たなビジネスチャンスの獲得に備えることができます。これらの動向を包括的に把握することで、企業は政治的、地理的、技術的、社会的、経済的な領域にわたる様々なリスクを軽減することができ、また、消費者行動とそれが製造コストや購買動向に与える影響をより明確に理解することができます。

• 市場促進要因
  • クラウドベース・テクノロジーの受け入れ拡大
  • 業界全体におけるビッグデータ分析の適用拡大
  • 量子コンピューティングの台頭とロボット工学におけるディープラーニングチップの実装強化
• 市場抑制要因
  • 熟練した専門知識と訓練を受けた専門家の不足
• 市場機会
  • 人間を意識したAIシステム開発の継続的ニーズ
  • 自律型ロボットの開発
• 市場の課題
  • 投資収益率の低下と利用可能な構造データの制限

ポーターの5つの力：ディープラーニングチップセット市場をナビゲートする戦略ツール

ポーターの5つの力フレームワークは、市場情勢の競合情勢を理解するための重要なツールです。ポーターのファイブフォース・フレームワークは、企業の競争力を評価し、戦略的機会を探るための明確な手法を提供します。このフレームワークは、企業が市場内の勢力図を評価し、新規事業の収益性を判断するのに役立ちます。これらの洞察により、企業は自社の強みを活かし、弱みに対処し、潜在的な課題を回避することができ、より強靭な市場でのポジショニングを確保することができます。

PESTLE分析：ディープラーニングチップセット市場における外部からの影響の把握

外部マクロ環境要因は、ディープラーニングチップセット市場の業績ダイナミクスを形成する上で極めて重要な役割を果たします。政治的、経済的、社会的、技術的、法的、環境的要因の分析は、これらの影響をナビゲートするために必要な情報を提供します。PESTLE要因を調査することで、企業は潜在的なリスクと機会をよりよく理解することができます。この分析により、企業は規制、消費者の嗜好、経済動向の変化を予測し、先を見越した積極的な意思決定を行う準備ができます。

市場シェア分析ディープラーニングチップセット市場における競合情勢の把握

ディープラーニングチップセット市場の詳細な市場シェア分析により、ベンダーの業績を包括的に評価することができます。企業は、収益、顧客ベース、成長率などの主要指標を比較することで、競争上のポジショニングを明らかにすることができます。この分析により、市場の集中、断片化、統合の動向が明らかになり、ベンダーは競争が激化する中で自社の地位を高める戦略的意思決定を行うために必要な知見を得ることができます。

FPNVポジショニング・マトリックスディープラーニングチップセット市場におけるベンダーのパフォーマンス評価

FPNVポジショニングマトリックスは、ディープラーニングチップセット市場においてベンダーを評価するための重要なツールです。このマトリックスにより、ビジネス組織はベンダーのビジネス戦略と製品満足度に基づき評価することで、目標に沿った十分な情報に基づいた意思決定を行うことができます。4つの象限によってベンダーを明確かつ正確にセグメント化し、戦略目標に最適なパートナーやソリューションを特定することができます。

戦略分析と推奨ディープラーニングチップセット市場における成功への道筋を描く

ディープラーニングチップセット市場の戦略分析は、世界市場でのプレゼンス強化を目指す企業にとって不可欠です。主要なリソース、能力、業績指標を見直すことで、企業は成長機会を特定し、改善に取り組むことができます。このアプローチにより、競合情勢における課題を克服し、新たなビジネスチャンスを活かして長期的な成功を収めるための体制を整えることができます。

本レポートでは、主要な注目分野を網羅した市場の包括的な分析を提供しています：

1.市場の浸透度：現在の市場環境の詳細なレビュー、主要企業による広範なデータ、市場でのリーチと全体的な影響力の評価。

2.市場の開拓度：新興市場における成長機会を特定し、既存分野における拡大可能性を評価し、将来の成長に向けた戦略的ロードマップを提供します。

3.市場の多様化：最近の製品発売、未開拓の地域、業界の主要な進歩、市場を形成する戦略的投資を分析します。

4.競合の評価と情報：競合情勢を徹底的に分析し、市場シェア、事業戦略、製品ポートフォリオ、認証、規制当局の承認、特許動向、主要企業の技術進歩などを検証します。

5.製品開発およびイノベーション：将来の市場成長を促進すると期待される最先端技術、研究開発活動、製品イノベーションをハイライトしています。

また、利害関係者が十分な情報を得た上で意思決定できるよう、重要な質問にも答えています：

1.現在の市場規模と今後の成長予測は？

2.最高の投資機会を提供する製品、セグメント、地域はどこか？

3.市場を形成する主な技術動向と規制の影響とは？

4.主要ベンダーの市場シェアと競合ポジションは？

5.ベンダーの市場参入・撤退戦略の原動力となる収益源と戦略的機会は何か？

目次

第1章 序文

第2章 調査手法

第3章 エグゼクティブサマリー

第4章 市場の概要

第5章 市場洞察

• 市場力学
  • 促進要因
    • クラウドベースの技術の普及
    • 業界全体でのビッグデータ分析の応用が増加
    • 量子コンピューティングの台頭とロボット工学におけるディープラーニングチップの実装強化
  • 抑制要因
    • 熟練した専門知識と訓練を受けた専門家の不足
  • 機会
    • 人間を認識するAIシステムの開発の必要性が継続
    • 自律型ロボットの新たな開発
  • 課題
    • 投資収益率の低下と利用可能な構造データの制限
• 市場セグメンテーション分析
• ポーターのファイブフォース分析
• PESTEL分析
  • 政治的
  • 経済
  • 社交
  • 技術的
  • 法律上
  • 環境

第6章 ディープラーニングチップセット市場：タイプ別

• 特定用途向け集積回路
• 中央処理装置
• フィールドプログラマブルゲートアレイ
• グラフィックスプロセッシングユニット

第7章 ディープラーニングチップセット市場：エンドユーザー別

• 航空宇宙および防衛
• 自動車
• 家電
• ヘルスケア
• 産業

第8章 南北アメリカのディープラーニングチップセット市場

• アルゼンチン
• ブラジル
• カナダ
• メキシコ
• 米国

第9章 アジア太平洋地域のディープラーニングチップセット市場

• オーストラリア
• 中国
• インド
• インドネシア
• 日本
• マレーシア
• フィリピン
• シンガポール
• 韓国
• 台湾
• タイ
• ベトナム

第10章 欧州・中東・アフリカのディープラーニングチップセット市場

• デンマーク
• エジプト
• フィンランド
• フランス
• ドイツ
• イスラエル
• イタリア
• オランダ
• ナイジェリア
• ノルウェー
• ポーランド
• カタール
• ロシア
• サウジアラビア
• 南アフリカ
• スペイン
• スウェーデン
• スイス
• トルコ
• アラブ首長国連邦
• 英国

第11章 競合情勢

• 市場シェア分析2023
• FPNVポジショニングマトリックス, 2023
• 競合シナリオ分析
• 戦略分析と提言

企業一覧

• Advanced Micro Devices, Inc.
• ARM Holdings
• Google LLC
• Graphcore
• Huawei Technologies
• Intel Corporation
• International Business Machines Corporation
• LG Electronics
• Mythic AI
• NVIDIA Corporation
• Qualcomm Technologies, Inc.
• Samsung Electronics Co., Ltd.
• Taiwan Semiconductor Manufacturing Company
• Xilinx, Inc.
• Zero ASIC Corporation

LIST OF FIGURES

• FIGURE 1. DEEP LEARNING CHIPSET MARKET RESEARCH PROCESS
• FIGURE 2. DEEP LEARNING CHIPSET MARKET SIZE, 2023 VS 2030
• FIGURE 3. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, 2018-2030 (USD MILLION)
• FIGURE 4. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY REGION, 2023 VS 2024 VS 2030 (USD MILLION)
• FIGURE 5. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY COUNTRY, 2023 VS 2024 VS 2030 (USD MILLION)
• FIGURE 6. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2023 VS 2030 (%)
• FIGURE 7. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2023 VS 2024 VS 2030 (USD MILLION)
• FIGURE 8. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2023 VS 2030 (%)
• FIGURE 9. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2023 VS 2024 VS 2030 (USD MILLION)
• FIGURE 10. AMERICAS DEEP LEARNING CHIPSET MARKET SIZE, BY COUNTRY, 2023 VS 2030 (%)
• FIGURE 11. AMERICAS DEEP LEARNING CHIPSET MARKET SIZE, BY COUNTRY, 2023 VS 2024 VS 2030 (USD MILLION)
• FIGURE 12. UNITED STATES DEEP LEARNING CHIPSET MARKET SIZE, BY STATE, 2023 VS 2030 (%)
• FIGURE 13. UNITED STATES DEEP LEARNING CHIPSET MARKET SIZE, BY STATE, 2023 VS 2024 VS 2030 (USD MILLION)
• FIGURE 14. ASIA-PACIFIC DEEP LEARNING CHIPSET MARKET SIZE, BY COUNTRY, 2023 VS 2030 (%)
• FIGURE 15. ASIA-PACIFIC DEEP LEARNING CHIPSET MARKET SIZE, BY COUNTRY, 2023 VS 2024 VS 2030 (USD MILLION)
• FIGURE 16. EUROPE, MIDDLE EAST & AFRICA DEEP LEARNING CHIPSET MARKET SIZE, BY COUNTRY, 2023 VS 2030 (%)
• FIGURE 17. EUROPE, MIDDLE EAST & AFRICA DEEP LEARNING CHIPSET MARKET SIZE, BY COUNTRY, 2023 VS 2024 VS 2030 (USD MILLION)
• FIGURE 18. DEEP LEARNING CHIPSET MARKET SHARE, BY KEY PLAYER, 2023
• FIGURE 19. DEEP LEARNING CHIPSET MARKET, FPNV POSITIONING MATRIX, 2023

LIST OF TABLES

• TABLE 1. DEEP LEARNING CHIPSET MARKET SEGMENTATION & COVERAGE
• TABLE 2. UNITED STATES DOLLAR EXCHANGE RATE, 2018-2023
• TABLE 3. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, 2018-2030 (USD MILLION)
• TABLE 4. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY REGION, 2018-2030 (USD MILLION)
• TABLE 5. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY COUNTRY, 2018-2030 (USD MILLION)
• TABLE 6. DEEP LEARNING CHIPSET MARKET DYNAMICS
• TABLE 7. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 8. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY APPLICATION SPECIFIC INTEGRATED CIRCUITS, BY REGION, 2018-2030 (USD MILLION)
• TABLE 9. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY CENTRAL PROCESSING UNITS, BY REGION, 2018-2030 (USD MILLION)
• TABLE 10. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY FIELD PROGRAMMABLE GATE ARRAYS, BY REGION, 2018-2030 (USD MILLION)
• TABLE 11. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY GRAPHICS PROCESSING UNITS, BY REGION, 2018-2030 (USD MILLION)
• TABLE 12. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 13. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY AEROSPACE & DEFENSE, BY REGION, 2018-2030 (USD MILLION)
• TABLE 14. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY AUTOMOTIVE, BY REGION, 2018-2030 (USD MILLION)
• TABLE 15. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY CONSUMER ELECTRONICS, BY REGION, 2018-2030 (USD MILLION)
• TABLE 16. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY HEALTHCARE, BY REGION, 2018-2030 (USD MILLION)
• TABLE 17. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY INDUSTRIAL, BY REGION, 2018-2030 (USD MILLION)
• TABLE 18. AMERICAS DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 19. AMERICAS DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 20. AMERICAS DEEP LEARNING CHIPSET MARKET SIZE, BY COUNTRY, 2018-2030 (USD MILLION)
• TABLE 21. ARGENTINA DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 22. ARGENTINA DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 23. BRAZIL DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 24. BRAZIL DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 25. CANADA DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 26. CANADA DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 27. MEXICO DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 28. MEXICO DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 29. UNITED STATES DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 30. UNITED STATES DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 31. UNITED STATES DEEP LEARNING CHIPSET MARKET SIZE, BY STATE, 2018-2030 (USD MILLION)
• TABLE 32. ASIA-PACIFIC DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 33. ASIA-PACIFIC DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 34. ASIA-PACIFIC DEEP LEARNING CHIPSET MARKET SIZE, BY COUNTRY, 2018-2030 (USD MILLION)
• TABLE 35. AUSTRALIA DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 36. AUSTRALIA DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 37. CHINA DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 38. CHINA DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 39. INDIA DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 40. INDIA DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 41. INDONESIA DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 42. INDONESIA DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 43. JAPAN DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 44. JAPAN DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 45. MALAYSIA DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 46. MALAYSIA DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 47. PHILIPPINES DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 48. PHILIPPINES DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 49. SINGAPORE DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 50. SINGAPORE DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 51. SOUTH KOREA DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 52. SOUTH KOREA DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 53. TAIWAN DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 54. TAIWAN DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 55. THAILAND DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 56. THAILAND DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 57. VIETNAM DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 58. VIETNAM DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 59. EUROPE, MIDDLE EAST & AFRICA DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 60. EUROPE, MIDDLE EAST & AFRICA DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 61. EUROPE, MIDDLE EAST & AFRICA DEEP LEARNING CHIPSET MARKET SIZE, BY COUNTRY, 2018-2030 (USD MILLION)
• TABLE 62. DENMARK DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 63. DENMARK DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 64. EGYPT DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 65. EGYPT DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 66. FINLAND DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 67. FINLAND DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 68. FRANCE DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 69. FRANCE DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 70. GERMANY DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 71. GERMANY DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 72. ISRAEL DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 73. ISRAEL DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 74. ITALY DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 75. ITALY DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 76. NETHERLANDS DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 77. NETHERLANDS DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 78. NIGERIA DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 79. NIGERIA DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 80. NORWAY DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 81. NORWAY DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 82. POLAND DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 83. POLAND DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 84. QATAR DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 85. QATAR DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 86. RUSSIA DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 87. RUSSIA DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 88. SAUDI ARABIA DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 89. SAUDI ARABIA DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 90. SOUTH AFRICA DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 91. SOUTH AFRICA DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 92. SPAIN DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 93. SPAIN DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 94. SWEDEN DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 95. SWEDEN DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 96. SWITZERLAND DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 97. SWITZERLAND DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 98. TURKEY DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 99. TURKEY DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 100. UNITED ARAB EMIRATES DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 101. UNITED ARAB EMIRATES DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 102. UNITED KINGDOM DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
• TABLE 103. UNITED KINGDOM DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
• TABLE 104. DEEP LEARNING CHIPSET MARKET SHARE, BY KEY PLAYER, 2023
• TABLE 105. DEEP LEARNING CHIPSET MARKET, FPNV POSITIONING MATRIX, 2023

The Deep Learning Chipset Market was valued at USD 10.23 billion in 2023, expected to reach USD 11.82 billion in 2024, and is projected to grow at a CAGR of 15.69%, to USD 28.39 billion by 2030.

The deep learning chipset market encompasses a dynamic intersection of artificial intelligence and hardware engineering, driving advancements in computational efficiency, speed, and capability. These chipsets, including GPUs, TPUs, neuromorphic chips, and FPGAs, power applications across diverse industries like automotive, healthcare, finance, and consumer electronics by enabling real-time data processing, complex problem solving, and automation. The necessity for deep learning chipsets is underscored by the escalating demand for AI-driven solutions that enhance decision-making, predictive analytics, and operational efficiency. End-use scope is broad, spanning autonomous driving systems, medical imaging diagnostics, personalized finance services, and smart devices, bolstered by the proliferation of IoT technologies and advancements in machine learning algorithms. Insights into key growth factors reveal that the increasing adoption of AI in end-user industries, growing investments in AI research and development, and the rise of smart infrastructures are pivotal influences. Emerging opportunities lie in the optimization of chip architectures for energy efficiency and speed, as well as the growing intersections between deep learning and quantum computing, which promise exponential improvements in processing power. However, the market faces challenges, including high development costs, technical complexities, and the need for a scalable infrastructure to support advanced AI workloads. Additionally, there are concerns over data privacy and security, which may impact market confidence. Critical areas of innovation include improving chip design for specific AI applications, enhancing neural network training efficacy, and embedding deep learning capabilities into edge devices, offering potential for niche markets and differentiation. For business growth, companies should focus on agile strategies that leverage collaborative partnerships and invest in skill development for cutting-edge chip design and AI deployment. The market's nature is competitive yet full of promise, with a landscape ripe for pioneering solutions that address both the technical limitations and growing demands of the AI revolution.

Market Dynamics: Unveiling Key Market Insights in the Rapidly Evolving Deep Learning Chipset Market

The Deep Learning Chipset Market is undergoing transformative changes driven by a dynamic interplay of supply and demand factors. Understanding these evolving market dynamics prepares business organizations to make informed investment decisions, refine strategic decisions, and seize new opportunities. By gaining a comprehensive view of these trends, business organizations can mitigate various risks across political, geographic, technical, social, and economic domains while also gaining a clearer understanding of consumer behavior and its impact on manufacturing costs and purchasing trends.

• Market Drivers
  • Growing acceptance of cloud-based technology
  • Increasing application of big data analytics across industries
  • Rising quantum computing and enhanced implementation of deep learning chips in robotics
• Market Restraints
  • Lack of skilled expertise and trained professional
• Market Opportunities
  • Ongoing need to develop human-aware AI systems
  • Emerging development of autonomous robots
• Market Challenges
  • Reduced return on investment and limited structural data available

Porter's Five Forces: A Strategic Tool for Navigating the Deep Learning Chipset Market

Porter's five forces framework is a critical tool for understanding the competitive landscape of the Deep Learning Chipset Market. It offers business organizations with a clear methodology for evaluating their competitive positioning and exploring strategic opportunities. This framework helps businesses assess the power dynamics within the market and determine the profitability of new ventures. With these insights, business organizations can leverage their strengths, address weaknesses, and avoid potential challenges, ensuring a more resilient market positioning.

PESTLE Analysis: Navigating External Influences in the Deep Learning Chipset Market

External macro-environmental factors play a pivotal role in shaping the performance dynamics of the Deep Learning Chipset Market. Political, Economic, Social, Technological, Legal, and Environmental factors analysis provides the necessary information to navigate these influences. By examining PESTLE factors, businesses can better understand potential risks and opportunities. This analysis enables business organizations to anticipate changes in regulations, consumer preferences, and economic trends, ensuring they are prepared to make proactive, forward-thinking decisions.

Market Share Analysis: Understanding the Competitive Landscape in the Deep Learning Chipset Market

A detailed market share analysis in the Deep Learning Chipset Market provides a comprehensive assessment of vendors' performance. Companies can identify their competitive positioning by comparing key metrics, including revenue, customer base, and growth rates. This analysis highlights market concentration, fragmentation, and trends in consolidation, offering vendors the insights required to make strategic decisions that enhance their position in an increasingly competitive landscape.

FPNV Positioning Matrix: Evaluating Vendors' Performance in the Deep Learning Chipset Market

The Forefront, Pathfinder, Niche, Vital (FPNV) Positioning Matrix is a critical tool for evaluating vendors within the Deep Learning Chipset Market. This matrix enables business organizations to make well-informed decisions that align with their goals by assessing vendors based on their business strategy and product satisfaction. The four quadrants provide a clear and precise segmentation of vendors, helping users identify the right partners and solutions that best fit their strategic objectives.

Strategy Analysis & Recommendation: Charting a Path to Success in the Deep Learning Chipset Market

A strategic analysis of the Deep Learning Chipset Market is essential for businesses looking to strengthen their global market presence. By reviewing key resources, capabilities, and performance indicators, business organizations can identify growth opportunities and work toward improvement. This approach helps businesses navigate challenges in the competitive landscape and ensures they are well-positioned to capitalize on newer opportunities and drive long-term success.

Key Company Profiles

The report delves into recent significant developments in the Deep Learning Chipset Market, highlighting leading vendors and their innovative profiles. These include Advanced Micro Devices, Inc., ARM Holdings, Google LLC, Graphcore, Huawei Technologies, Intel Corporation, International Business Machines Corporation, LG Electronics, Mythic AI, NVIDIA Corporation, Qualcomm Technologies, Inc., Samsung Electronics Co., Ltd., Taiwan Semiconductor Manufacturing Company, Xilinx, Inc., and Zero ASIC Corporation.

Market Segmentation & Coverage

This research report categorizes the Deep Learning Chipset Market to forecast the revenues and analyze trends in each of the following sub-markets:

• Based on Type, market is studied across Application Specific Integrated Circuits, Central Processing Units, Field Programmable Gate Arrays, and Graphics Processing Units.
• Based on End-User, market is studied across Aerospace & Defense, Automotive, Consumer Electronics, Healthcare, and Industrial.
• Based on Region, market is studied across Americas, Asia-Pacific, and Europe, Middle East & Africa. The Americas is further studied across Argentina, Brazil, Canada, Mexico, and United States. The United States is further studied across California, Florida, Illinois, New York, Ohio, Pennsylvania, and Texas. The Asia-Pacific is further studied across Australia, China, India, Indonesia, Japan, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand, and Vietnam. The Europe, Middle East & Africa is further studied across Denmark, Egypt, Finland, France, Germany, Israel, Italy, Netherlands, Nigeria, Norway, Poland, Qatar, Russia, Saudi Arabia, South Africa, Spain, Sweden, Switzerland, Turkey, United Arab Emirates, and United Kingdom.

The report offers a comprehensive analysis of the market, covering key focus areas:

1. Market Penetration: A detailed review of the current market environment, including extensive data from top industry players, evaluating their market reach and overall influence.

2. Market Development: Identifies growth opportunities in emerging markets and assesses expansion potential in established sectors, providing a strategic roadmap for future growth.

3. Market Diversification: Analyzes recent product launches, untapped geographic regions, major industry advancements, and strategic investments reshaping the market.

4. Competitive Assessment & Intelligence: Provides a thorough analysis of the competitive landscape, examining market share, business strategies, product portfolios, certifications, regulatory approvals, patent trends, and technological advancements of key players.

5. Product Development & Innovation: Highlights cutting-edge technologies, R&D activities, and product innovations expected to drive future market growth.

The report also answers critical questions to aid stakeholders in making informed decisions:

1. What is the current market size, and what is the forecasted growth?

2. Which products, segments, and regions offer the best investment opportunities?

3. What are the key technology trends and regulatory influences shaping the market?

4. How do leading vendors rank in terms of market share and competitive positioning?

5. What revenue sources and strategic opportunities drive vendors' market entry or exit strategies?

Table of Contents

1. Preface

• 1.1. Objectives of the Study
• 1.2. Market Segmentation & Coverage
• 1.3. Years Considered for the Study
• 1.4. Currency & Pricing
• 1.5. Language
• 1.6. Stakeholders

2. Research Methodology

• 2.1. Define: Research Objective
• 2.2. Determine: Research Design
• 2.3. Prepare: Research Instrument
• 2.4. Collect: Data Source
• 2.5. Analyze: Data Interpretation
• 2.6. Formulate: Data Verification
• 2.7. Publish: Research Report
• 2.8. Repeat: Report Update

3. Executive Summary

4. Market Overview

5. Market Insights

• 5.1. Market Dynamics
  • 5.1.1. Drivers
    • 5.1.1.1. Growing acceptance of cloud-based technology
    • 5.1.1.2. Increasing application of big data analytics across industries
    • 5.1.1.3. Rising quantum computing and enhanced implementation of deep learning chips in robotics
  • 5.1.2. Restraints
    • 5.1.2.1. Lack of skilled expertise and trained professional
  • 5.1.3. Opportunities
    • 5.1.3.1. Ongoing need to develop human-aware AI systems
    • 5.1.3.2. Emerging development of autonomous robots
  • 5.1.4. Challenges
    • 5.1.4.1. Reduced return on investment and limited structural data available
• 5.2. Market Segmentation Analysis
• 5.3. Porter's Five Forces Analysis
  • 5.3.1. Threat of New Entrants
  • 5.3.2. Threat of Substitutes
  • 5.3.3. Bargaining Power of Customers
  • 5.3.4. Bargaining Power of Suppliers
  • 5.3.5. Industry Rivalry
• 5.4. PESTLE Analysis
  • 5.4.1. Political
  • 5.4.2. Economic
  • 5.4.3. Social
  • 5.4.4. Technological
  • 5.4.5. Legal
  • 5.4.6. Environmental

6. Deep Learning Chipset Market, by Type

• 6.1. Introduction
• 6.2. Application Specific Integrated Circuits
• 6.3. Central Processing Units
• 6.4. Field Programmable Gate Arrays
• 6.5. Graphics Processing Units

7. Deep Learning Chipset Market, by End-User

• 7.1. Introduction
• 7.2. Aerospace & Defense
• 7.3. Automotive
• 7.4. Consumer Electronics
• 7.5. Healthcare
• 7.6. Industrial

8. Americas Deep Learning Chipset Market

• 8.1. Introduction
• 8.2. Argentina
• 8.3. Brazil
• 8.4. Canada
• 8.5. Mexico
• 8.6. United States

9. Asia-Pacific Deep Learning Chipset Market

• 9.1. Introduction
• 9.2. Australia
• 9.3. China
• 9.4. India
• 9.5. Indonesia
• 9.6. Japan
• 9.7. Malaysia
• 9.8. Philippines
• 9.9. Singapore
• 9.10. South Korea
• 9.11. Taiwan
• 9.12. Thailand
• 9.13. Vietnam

10. Europe, Middle East & Africa Deep Learning Chipset Market

• 10.1. Introduction
• 10.2. Denmark
• 10.3. Egypt
• 10.4. Finland
• 10.5. France
• 10.6. Germany
• 10.7. Israel
• 10.8. Italy
• 10.9. Netherlands
• 10.10. Nigeria
• 10.11. Norway
• 10.12. Poland
• 10.13. Qatar
• 10.14. Russia
• 10.15. Saudi Arabia
• 10.16. South Africa
• 10.17. Spain
• 10.18. Sweden
• 10.19. Switzerland
• 10.20. Turkey
• 10.21. United Arab Emirates
• 10.22. United Kingdom

11. Competitive Landscape

• 11.1. Market Share Analysis, 2023
• 11.2. FPNV Positioning Matrix, 2023
• 11.3. Competitive Scenario Analysis
• 11.4. Strategy Analysis & Recommendation

Companies Mentioned

• 1. Advanced Micro Devices, Inc.
• 2. ARM Holdings
• 3. Google LLC
• 4. Graphcore
• 5. Huawei Technologies
• 6. Intel Corporation
• 7. International Business Machines Corporation
• 8. LG Electronics
• 9. Mythic AI
• 10. NVIDIA Corporation
• 11. Qualcomm Technologies, Inc.
• 12. Samsung Electronics Co., Ltd.
• 13. Taiwan Semiconductor Manufacturing Company
• 14. Xilinx, Inc.
• 15. Zero ASIC Corporation