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1459350

全球神經處理器市場 - 2024-2031

Global Neural Processor Market - 2024-2031

出版日期: 2024年04月03日 | 出版商:

DataM Intelligence | 英文 180 Pages | 商品交期: 約2個工作天內

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簡介目錄

概述

全球神經處理器市場在 2023 年達到 2.243 億美元，預計到 2031 年將達到 8.827 億美元，2024-2031 年預測期間複合CAGR為 18.8%。

神經處理器對於電腦視覺和自主系統等應用至關重要，因為它們具有加速訓練和推理等深度學習任務的獨特能力。對於邊緣運算系統來說，高效和低延遲的處理解決方案是必要的，邊緣運算系統處理更靠近來源或端點設備的資料。高效能、高能源效率的神經處理器非常適合邊緣運算部署，可在網路邊緣為無人駕駛汽車、智慧城市和物聯網設備等應用提供人工智慧推理。

在網路邊緣（包括邊緣伺服器到物聯網設備和感測器）處理資料稱為邊緣運算。神經處理器對於即時人工智慧推理和邊緣決策至關重要，因為它們提供低延遲和高效能運算能力。工業自動化、無人駕駛汽車和智慧城市等領域的邊緣運算應用的成長推動了神經處理器的需求。

由於主要參與者對神經處理器開發的投資增加，神經處理器的採用不斷增加，北美正在主導市場。主要參與者對神經處理器的投資不斷增加，有助於推動預測期內區域市場的成長。例如，2024 年3 月20 日，汽車技術公司Indie Semiconductor, Inc. 投資了Expedera Inc.。此次合作將為針對高級駕駛輔助系統(ADAS) 的感測解決方案提供客製化的人工智慧處理能力，並包括商業協議將客製化的 Expedera Origin NPU 處理解決方案整合到未來的獨立產品中。

動力學

技術進步

半導體技術、架構設計和電源管理的進步有助於節能神經處理器的發展。降低功耗和最佳化能源利用率使神經處理器適合需要低功耗解決方案的應用，例如行動裝置、邊緣運算設備、物聯網端點和電池供電系統。節能的神經處理器吸引了尋求經濟高效且環保的人工智慧解決方案的客戶。

技術進步使神經處理器能夠在處理核心、記憶體容量和運算資源方面進行擴展。可擴展的架構允許製造商提供具有不同效能等級和配置的神經處理器，以滿足不同的客戶需求。設計和客製化選項的靈活性進一步增強了市場競爭力和客戶滿意度。英特爾將最佳化融入開發人員使用的人工智慧框架中，並提供基礎庫，使使用在各種硬體類型上都具有高性能和可移植性，從而使人工智慧硬體技術盡可能易於存取和方便用戶使用。

對人工智慧 (AI) 應用的需求不斷成長

推動神經處理器市場的主要因素之一是人工智慧應用在各行業的傳播，包括醫療保健、銀行、汽車、零售和製造。自然語言處理（NLP）、預測、影像辨識和其他高階能力都是透過神經處理器實現的，神經處理器是人工智慧演算法、深度學習模型和機器學習任務的大腦。數位來源、物聯網設備和其他來源創建的資料呈指數級成長，推動了神經處理器的需求。處理器對於巨量資料分析和即時資料處理應用至關重要，因為它們旨在處理大量資料並執行複雜的計算。

邊緣運算架構變得越來越普遍，特別是在物聯網部署中，其中人工智慧處理發生在距離資料來源或端點設備更近的地方。對於邊緣人工智慧應用，具有低功耗和強大運算能力的神經處理器非常適合。它允許即時資料處理、邊緣人工智慧推理、降低延遲並提高物聯網生態系統的效率。

神經處理器的需求在一定程度上是由邊緣人工智慧設定的成長所推動的。神經處理器被雲端服務供應商和AI服務平台用來為開發者和企業提供AI服務和解決方案。透過使用神經處理器，聊天機器人、情緒分析、推薦引擎、語音識別、語言翻譯和資料分析等基於雲端的人工智慧應用變得更加高效、可擴展且經濟實惠。

開發成本高

作為新進者，特別是資金有限的小型企業或新創企業，高昂的開發成本為進入者設置了障礙。因此，市場競爭空間較小，可能導致市場佔有率集中於成熟企業，以及產品供應的創新和多樣性減少。由於支出過高，旨在開發神經處理技術的研究與開發（R&D）計畫無法獲得資助。這可能會導致引入新功能或增強功能的延遲、創新週期更長以及產品缺乏特色。

為了收回大量的開發支出，製造商將不得不提高神經處理器的價格。在價格敏感的市場群體中，這可能會降低產品的競爭力並阻礙其市場滲透，特別是在新興經濟體或經濟產業。企業必須投入大量的財力、人力資本和時間來開發神經處理器。組織的整體成長和競爭力都會受到這種資源分配的影響，這可能會使他們遠離其他關鍵領域，例如客戶服務、行銷、銷售和生態系統合作夥伴關係。

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

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