ニューラルプロセッサーの世界市場-2024-2031年

概要

ニューラルプロセッサーの世界市場は、2023年に2億2,430万米ドルに達し、2031年には8億8,270万米ドルに達すると予測され、予測期間2024-2031年のCAGRは18.8%で成長します。

ニューラルプロセッサーは、トレーニングや推論などの深層学習タスクを高速化する独自の能力を備えているため、コンピュータ・ビジョンや自律システムなどのアプリケーションにとって極めて重要です。よりソースやエンドポイントデバイスに近いところでデータを扱うエッジコンピューティングシステムには、効率性と低レイテンシーを両立する処理ソリューションが必要です。高性能でエネルギー効率に優れたニューラルプロセッサーは、エッジコンピューティングの導入に理想的であり、ドライバーレスカー、スマートシティ、IoTデバイスなどのアプリケーション向けに、ネットワークエッジでのAI推論を可能にします。

エッジサーバーからIoTデバイスやセンサーまで、ネットワークのエッジでデータを処理することは、エッジコンピューティングとして知られています。ニューラルプロセッサーは、低レイテンシーで高性能なコンピューティング機能を提供するため、AIの推論やエッジでの意思決定をリアルタイムで行うために不可欠です。ニューラルプロセッサーの需要は、産業オートメーション、無人運転車、スマートシティなどの領域におけるエッジコンピューティングアプリケーションの成長が牽引しています。

北米が市場を独占しているのは、ニューラルプロセッサーの開発に投資する主要プレーヤーの増加により、ニューラルプロセッサーの採用が拡大しているためです。主要キープレイヤーによるニューラルプロセッサーへの投資の増加は、予測期間における地域市場の成長を後押しします。例えば、2024年3月20日、自動車技術企業のIndie Semiconductor, Inc.は、Expedera Inc.に投資しました。このパートナーシップは、ADAS（先進運転支援システム）をターゲットとするセンシングソリューション向けにカスタマイズされた人工知能対応処理機能を提供するもので、カスタマイズされたExpedera Origin NPU処理ソリューションを将来のインディ製品に統合する商業契約も含まれています。

ダイナミクス

技術的進歩

半導体技術、アーキテクチャ設計、電力管理の進歩は、エネルギー効率の高いニューラルプロセッサーの開発に貢献しています。消費電力の削減とエネルギー利用の最適化により、ニューラルプロセッサーは、モバイル機器、エッジコンピューティング機器、IoTエンドポイント、バッテリー駆動システムなど、低消費電力ソリューションを必要とするアプリケーションに適しています。エネルギー効率の高いニューラルプロセッサーは、費用対効果が高く、環境に優しいAIソリューションを求める顧客を惹きつけます。

技術の進歩により、ニューラルプロセッサーはプロセッシングコア、メモリー容量、計算リソースの拡張が可能になっています。スケーラブルなアーキテクチャにより、メーカーはさまざまな性能レベルと構成を持つニューラルプロセッサーを提供し、多様な顧客の要求に応えることができます。設計とカスタマイズオプションの柔軟性は、市場競争力と顧客満足度をさらに高めます。インテルは、開発者が利用するAIフレームワークに最適化を組み込み、AIハードウェア技術を可能な限り利用しやすく、ユーザーフレンドリーなものにするため、さまざまな種類のハードウェアで高い性能と移植性を発揮する基本的なライブラリを提供しています。

人工知能（AI）アプリケーションの需要増加

ニューラルプロセッサーの市場を後押ししている主な要因の1つは、ヘルスケア、銀行、自動車、小売、製造など、さまざまな業界でAIアプリケーションが普及していることです。自然言語処理（NLP）、予測、画像認識、その他の高度な能力は、AIアルゴリズム、深層学習モデル、機械学習タスクの頭脳であるニューラルプロセッサーによって実現されています。ニューラルプロセッサーの需要は、デジタルソースやIoTデバイスなどから作成されるデータの急激な増加によって牽引されています。プロセッサーは大量のデータを処理し、複雑な計算を実行するように設計されているため、ビッグデータ分析やリアルタイムのデータ処理アプリケーションには不可欠です。

エッジコンピューティングアーキテクチャは、特にIoTの展開において一般的になりつつあり、AI処理はデータソースやエンドポイントデバイスの近くで行われます。エッジAIアプリケーションには、低消費電力で優れた演算能力を持つニューラルプロセッサーが理想的です。これにより、IoTエコシステムにおけるリアルタイムのデータ処理、エッジAI推論、低レイテンシー、効率の向上が可能になります。

ニューラルプロセッサー需要は、エッジAIセットアップの成長によって一部煽られています。ニューラルプロセッサーは、クラウドサービスプロバイダーやAIサービスプラットフォームによって使用され、開発者や企業にAIサービスやソリューションを提供しています。チャットボット、感情分析、レコメンデーションエンジン、音声認識、言語翻訳、データ分析などのクラウドベースのAIアプリケーションは、ニューラルプロセッサーを使用することで、より効率的でスケーラブルかつ手頃な価格になっています。

高い開発コスト

新規参入者、特に資金が限られている中小企業や新興企業にとって、高い開発コストは参入の障害となります。その結果、市場での競合の余地が少なくなり、老舗企業への市場シェアの集中や、製品提供の革新性や多様性の低下につながる可能性があります。神経処理技術の開発を目的とする研究開発（R&D）プロジェクトは、高額な支出を理由に資金援助を受けることができないです。その結果、新機能や機能強化の導入の遅れ、技術革新のサイクルの長期化、製品の差別化の欠如が生じる可能性があります。

多額の開発費を回収するために、メーカーはニューラルプロセッサーを値上げせざるを得なくなります。価格に敏感な市場グループにおいては、これは製品の競合力を低下させ、特に新興経済国や経済産業における市場浸透を妨げるかもしれないです。ニューラルプロセッサーを開発するために、企業は多くの資金、人材、時間を割かなければならないです。組織の成長と競争力全体は、この資源配分によって影響を受け、顧客サービス、マーケティング、販売、エコシステムパートナーシップなど、他の重要な分野から資源が奪われる可能性があります。

目次

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
    • 技術の進歩
    • 人工知能（AI）アプリケーションの需要増加
  • 抑制要因
    • 高い開発コスト
  • 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の見解

第6章 COVID-19分析

第7章 アプリケーション別

• 不正検知
• ハードウェア診断
• 財務予測
• イメージ最適化
• その他

第8章 エンドユーザー別

• BFSI
• ヘルスケア
• 小売
• 防衛機関
• 物流
• その他

第9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
  • スペイン
  • 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
  • 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10章 競合情勢

• 競合シナリオ
• 市況/シェア分析
• M&A分析

第11章 企業プロファイル

• Google 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
• Intel corporation
• Qualcomm Technologies, Inc.
• Ceva, Inc.
• BrainChip, Inc.
• NVIDIA Corporation
• Graphcore
• Hewlett Packard Enterprise Development LP
• HRL Laboratories, LLC
• Ceva, Inc.

第12章 付録

Overview

Global Neural Processor Market reached US$ 224.3 Million in 2023 and is expected to reach US$ 882.7 Million by 2031, growing with a CAGR of 18.8% during the forecast period 2024-2031.

Neural processors are crucial for applications such as computer vision and autonomous systems because of their unique ability to speed up deep learning tasks like training and inference. Processing solutions that are both efficient and low latency are necessary for edge computing systems, which handle data closer to the source or endpoint devices. High-performance and energy-efficient neural processors are ideal for edge computing deployments, enabling AI inference at the network edge for applications like driverless cars, smart cities and Internet of Things devices.

Processing data at the edge of the network, including edge servers to IoT devices and sensors, is known as edge computing. Neural processors are essential for allowing AI inference and edge decision-making in real time because they offer low latency and high-performance computing capabilities. Neural processor demand is driven by the growth of edge computing applications in domains such as industrial automation, driverless cars and smart cities.

North America is dominating the market due to the growing adoption of neural processors due to the increase in the major key player's investment in the development of neural processors. The growing investment by major key players for the neural processor helps to boost regional market growth over the forecast period. For instance, on March 20, 2024, indie Semiconductor, Inc., an auto-tech company invested in Expedera Inc. The partnership will deliver customized artificial intelligence-enabled processing capabilities for sensing solutions targeting Advanced Driver Assistance Systems (ADAS) and includes a commercial agreement to integrate customized Expedera Origin NPU processing solutions into future indie products.

Dynamics

Technological Advancements

Advancements in semiconductor technology, architecture design and power management contribute to the development of energy-efficient neural processors. Reduced power consumption and optimized energy utilization make neural processors suitable for applications requiring low-power solutions, such as mobile devices, edge computing devices, IoT endpoints and battery-powered systems. Energy-efficient neural processors attract customers seeking cost-effective and environmentally friendly AI solutions.

Technological advancements enable neural processors to scale in terms of processing cores, memory capacity and computational resources. Scalable architectures allow manufacturers to offer neural processors with varying performance levels and configurations to meet diverse customer requirements. Flexibility in design and customization options further enhances market competitiveness and customer satisfaction. Intel incorporates optimizations into the AI frameworks utilized by developers and provides fundamental libraries to make uses highly performant and portable across various hardware types to make AI hardware technologies as accessible and user-friendly as feasible.

Increasing Demand for Artificial Intelligence (AI) Applications

One of the main factors propelling the market for neural processors is the spread of AI applications in a variety of industries, including healthcare, banking, automotive, retail and manufacturing. Natural language processing (NLP), forecasting, picture recognition and other advanced abilities are made possible by neural processors, which are the brains of AI algorithms, deep learning models and machine learning tasks. Neural processor demand has been driven by the exponential rise of data created from digital sources, IoT devices and other sources. The processors are essential to big data analytics and real-time data processing applications since they are designed to handle massive amounts of data and carry out intricate calculations.

Edge computing architectures are becoming increasingly common, particularly in Internet of Things deployments, where AI processing occurs closer to the data source or endpoint devices. For edge AI applications, neural processors with low power consumption and great computing power are ideally suited. It allow for real-time data processing, edge AI inference, lower latency and increased efficiency in IoT ecosystems.

Neural processor demand is fueled in part by the growth of edge AI setups. Neural processors are used by cloud service providers and AI service platforms to provide developers and businesses with AI services and solutions. Cloud-based AI applications like chatbots, sentiment analysis, recommendation engines, speech recognition, language translation and data analytics have been rendered more efficient, scalable and affordable by using neural processors.

High Development Costs

As new entrants, particularly smaller businesses or startups with limited funding, the high development costs provide obstacles to the entrance. As a result, there is less room for competition in the market, which might lead to a concentration of market share among well-established businesses as well as fewer innovations and variety in product offers. Research and development (R&D) projects aiming at developing neural processing technology are discouraged from receiving funding because of high expenditures. Delays in introducing new features or enhancements, longer cycles of innovation and a lack of product distinction might result from this.

To recover the significant development expenditures, manufacturers will have to increase the price of their neural processors. In price-sensitive market groups, this might reduce the competitiveness of the products and hinder their market penetration, especially in emerging economies or economic industries. Businesses have to give a large amount of their financial resources, human capital and time to the development of neural processors. The entire growth and competitiveness of the organization are impacted by this allocation of resources, which could take them away from other critical areas like customer service, marketing, sales and ecosystem partnerships.

Segment Analysis

The global neural processor market is segmented based on application, end-user and region.

Growing Adoption of Neural Processor in Fraud Detection

Based on the application, the neural processor market is segmented into fraud detection, hardware diagnostics, financial forecasting, image optimization and others.

As neural processors are exceptionally proficient at pattern recognition, they are very useful for recognizing trends and abnormalities that point to fraud. It examine enormous volumes of data from several sources, like network activity and financial transactions, to spot unusual trends that help to detect fraud. Real-time fraud detection capabilities are made possible by neural processors, which provide organizations the ability to identify and stop fraudulent activity as it occurs. Decisions are taken quickly and proactive fraud protection measures can implemented because of neural processors' efficiency and speed in analyzing massive datasets in actual time.

On February 01, 2024, Mastercard launched a generative AI model that helps to boost fraud detection by up to 300%. The company claims that it has built its own AI model that helps various banks detect bank fraud. Complex behavioral analysis, including anomaly identification and user behavior profiling, may be carried out via neural processors. Neural processors can detect abnormalities in user behavior that can point to fraudulent activity by examining patterns in user behavior, such as past transactions, login habits and travel pathways.

Geographical Penetration

North America is Dominating the Neural Processor Market

Research and development in artificial intelligence (AI), machine learning and semiconductor technologies focuses on North America. Leading technology companies, research centers and startups that propel advances in neural processing designs, algorithms and applications are based in the region. The semiconductor and artificial intelligence industries in the region are flourishing because of collaboration between government, business, academic institutions and venture capital companies. The ecosystem promotes the creation of neural processing solutions for a range of applications, encourages innovation and accelerates up technology transfer.

Numerous of the top semiconductor companies, producers of AI chips and global technological giants have their headquarters or a major presence in North America. The businesses such as NVIDIA, Intel, AMD, Google, Apple, Qualcomm, IBM and Apple are essential in advancing the use of neural processors in a variety of sectors. The semiconductor and AI industries receive a lot of money and investments from North America.

Competitive Landscape.

The major global players in the market include Google Inc., Intel corporation, Qualcomm Technologies, Inc., Ceva, Inc., BrainChip, Inc., NVIDIA Corporation, Graphcore, Hewlett Packard Enterprise Development LP, HRL Laboratories, LLC and Ceva, Inc.

COVID-19 Impact Analysis

COVID-19 created disruptions to globally supply chains, which affected the major key players of semiconductors. Manufacturers of neural processors encountered challenges in sourcing raw materials and logistical issues that affected the supply of neural processors to the market. In many organizations, the pandemic accelerated the digital transformation. The has increased demand for machine learning and artificial intelligence technologies, including neural processors. E-commerce and remote work all saw notable increases during the pandemic.

The use of AI-powered applications in the healthcare sector such as medical imaging analysis and patient monitoring, increased significantly during the pandemic. Large healthcare datasets were processed quickly by neural processors, which also helped to speed up research and enhance patient outcomes. Neural processors saw growing popularity in edge devices for real-time AI inference and processing with the rise of IoT devices and edge computing solutions. Neural processors that are additionally powerful and efficient are needed for edge AI applications that are becoming increasingly popular in smart cities, driverless cars, industrial automation and Internet of Things sensors.

Russia-Ukraine War Impact Analysis

The issue has the potential to disrupt semiconductor manufacturers' supply networks, especially those that manufacture neural processors. With its semiconductor manufacturing facilities, Russia and Ukraine both contribute to the world's chip production. Any interruptions to these facilities or logistical systems result in a scarcity of supplies, which would affect the global availability of neural processors.

Neural processing and artificial intelligence (AI) technology see a rise in demand for military applications as the war contributes to military operations and defense capabilities. Defense contractors and government organizations are experiencing a spike in demand for these processors since they are utilized in drones, surveillance systems, autonomous vehicles and other defense-related technology.

The conflict causes geopolitical tensions that result in trade restrictions, export controls or sanctions on the export of technology, particularly neural processors. The has an impact on the global commerce of semiconductor technology and parts, restricting market access and creating uncertainty for companies that make brain processors globally. Technology development objectives change as a result of the war, with a stronger emphasis on neural processing applications for the military and defense sectors. Research and development activities refocused on improving AI capabilities for military applications, which might affect how the neural processor industry is evolving in terms of innovation.

By Application

• Fraud Detection
• Hardware Diagnostics
• Financial Forecasting
• Image Optimization
• Other

By End-User

• BFSI
• Healthcare
• Retail
• Defense Agencies
• Logistics
• Other

By Region

• North America
  • U.S.
  • Canada
  • Mexico
• Europe
  • Germany
  • UK
  • France
  • Italy
  • Spain
  • 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 December 16, 2023, Intel launched Core Ultra processors for 'AI PCs' with a dedicated NPU. At its "AI Everywhere" introduction event, this is the first batch of consumer-segment processors with a dedicated neural processing unit (NPU), enabling on-device generative AI experience.
• On October 23, 2023, Neurxcore, launched a neural processor in the market. The product line, according to the business, utilizes NVIDIA's research and shows notable gains in energy economy, performance and feature set when compared to the original NVIDIA version.
• On April 19, 2022, Synopsys, Inc. launched Industry's Highest Performance Neural Processor IP in the market. With up to 96K MACs with improved utilization, new sparsity capabilities and a new interconnect for scalability, the DesignWare ARC NPX6 NPU IP offers industry-leading performance and power efficiency of 30 TOPS/Watt.

Why Purchase the Report?

• To visualize the global neural processor market segmentation based on 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 neural processor 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 neural processor market report would provide approximately 54 tables, 48 figures and 380 Pages.

Target Audience 2024

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

Table of Contents

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 Application
• 3.2.Snippet by End-User
• 3.3.Snippet by Region

4.Dynamics

• 4.1.Impacting Factors
  • 4.1.1.Drivers
    • 4.1.1.1.Technological Advancements
    • 4.1.1.2.Increasing Demand for Artificial Intelligence (AI) Applications
  • 4.1.2.Restraints
    • 4.1.2.1.High Development Costs
  • 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 Application

• 7.1.Introduction
  • 7.1.1.Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
  • 7.1.2.Market Attractiveness Index, By Application
• 7.2.Fraud Detection*
  • 7.2.1.Introduction
  • 7.2.2.Market Size Analysis and Y-o-Y Growth Analysis (%)
• 7.3.Hardware Diagnostics
• 7.4.Financial Forecasting
• 7.5.Image Optimization
• 7.6.Other

8.By End-User

• 8.1.Introduction
  • 8.1.1.Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
  • 8.1.2.Market Attractiveness Index, By End-User
• 8.2.BFSI*
  • 8.2.1.Introduction
  • 8.2.2.Market Size Analysis and Y-o-Y Growth Analysis (%)
• 8.3.Healthcare
• 8.4.Retail
• 8.5.Defense Agencies
• 8.6.Logistics
• 8.7.Other

9.By Region

• 9.1.Introduction
  • 9.1.1.Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
  • 9.1.2.Market Attractiveness Index, By Region
• 9.2.North America
  • 9.2.1.Introduction
  • 9.2.2.Key Region-Specific Dynamics
  • 9.2.3.Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
  • 9.2.4.Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
  • 9.2.5.Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
    • 9.2.5.1.U.S.
    • 9.2.5.2.Canada
    • 9.2.5.3.Mexico
• 9.3.Europe
  • 9.3.1.Introduction
  • 9.3.2.Key Region-Specific Dynamics
  • 9.3.3.Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
  • 9.3.4.Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
  • 9.3.5.Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
    • 9.3.5.1.Germany
    • 9.3.5.2.UK
    • 9.3.5.3.France
    • 9.3.5.4.Italy
    • 9.3.5.5.Spain
    • 9.3.5.6.Rest of Europe
• 9.4.South America
  • 9.4.1.Introduction
  • 9.4.2.Key Region-Specific Dynamics
  • 9.4.3.Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
  • 9.4.4.Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
  • 9.4.5.Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
    • 9.4.5.1.Brazil
    • 9.4.5.2.Argentina
    • 9.4.5.3.Rest of South America
• 9.5.Asia-Pacific
  • 9.5.1.Introduction
  • 9.5.2.Key Region-Specific Dynamics
  • 9.5.3.Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
  • 9.5.4.Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
  • 9.5.5.Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
    • 9.5.5.1.China
    • 9.5.5.2.India
    • 9.5.5.3.Japan
    • 9.5.5.4.Australia
    • 9.5.5.5.Rest of Asia-Pacific
• 9.6.Middle East and Africa
  • 9.6.1.Introduction
  • 9.6.2.Key Region-Specific Dynamics
  • 9.6.3.Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
  • 9.6.4.Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User

10.Competitive Landscape

• 10.1.Competitive Scenario
• 10.2.Market Positioning/Share Analysis
• 10.3.Mergers and Acquisitions Analysis

11.Company Profiles

• 11.1.Google Inc.*
  • 11.1.1.Company Overview
  • 11.1.2.Product Portfolio and Description
  • 11.1.3.Financial Overview
  • 11.1.4.Key Developments
• 11.2.Intel corporation
• 11.3.Qualcomm Technologies, Inc.
• 11.4.Ceva, Inc.
• 11.5.BrainChip, Inc.
• 11.6.NVIDIA Corporation
• 11.7.Graphcore
• 11.8.Hewlett Packard Enterprise Development LP
• 11.9.HRL Laboratories, LLC
• 11.10.Ceva, Inc.

LIST NOT EXHAUSTIVE

12.Appendix

• 12.1.About Us and Services
• 12.2.Contact Us