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1459350

全球神经处理器市场 - 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: 约2个工作天内

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简介目录

概述

全球神经处理器市场在 2023 年达到 2.243 亿美元，预计到 2031 年将达到 8.827 亿美元，2024-2031 年预测期间复合CAGR为 18.8%。

神经处理器对于电脑视觉和自主系统等应用至关重要，因为它们具有加速训练和推理等深度学习任务的独特能力。对于边缘运算系统来说，高效和低延迟的处理解决方案是必要的，边缘运算系统处理更靠近来源或端点设备的资料。高效能、高能源效率的神经处理器非常适合边缘运算部署，可在网路边缘为无人驾驶汽车、智慧城市和物联网设备等应用提供人工智慧推理。

在网路边缘（包括边缘伺服器到物联网设备和感测器）处理资料称为边缘运算。神经处理器对于即时人工智慧推理和边缘决策至关重要，因为它们提供低延迟和高效能运算能力。工业自动化、无人驾驶汽车和智慧城市等领域的边缘运算应用的成长推动了神经处理器的需求。

由于主要参与者对神经处理器开发的投资增加，神经处理器的采用不断增加，北美正在主导市场。主要参与者对神经处理器的投资不断增加，有助于推动预测期内区域市场的成长。例如，2024 年3 月20 日，汽车技术公司Indie Semiconductor, Inc. 投资了Expedera Inc.。此次合作将为针对高级驾驶辅助系统(ADAS) 的感测解决方案提供客製化的人工智慧处理能力，并包括商业协议将客製化的 Expedera Origin NPU 处理解决方案整合到未来的独立产品中。

动力学

技术进步

半导体技术、架构设计和电源管理的进步有助于节能神经处理器的发展。降低功耗和优化能源利用率使神经处理器适合需要低功耗解决方案的应用，例如行动装置、边缘运算设备、物联网端点和电池供电系统。节能的神经处理器吸引了寻求经济高效且环保的人工智慧解决方案的客户。

技术进步使神经处理器能够在处理核心、记忆体容量和运算资源方面进行扩展。可扩展的架构允许製造商提供具有不同效能等级和配置的神经处理器，以满足不同的客户需求。设计和客製化选项的灵活性进一步增强了市场竞争力和客户满意度。英特尔将优化融入开发人员使用的人工智慧框架中，并提供基础库，使使用在各种硬体类型上都具有高性能和可移植性，从而使人工智慧硬体技术尽可能易于存取和用户友好。

对人工智慧 (AI) 应用的需求不断增长

推动神经处理器市场的主要因素之一是人工智慧应用在各行业的传播，包括医疗保健、银行、汽车、零售和製造。自然语言处理（NLP）、预测、影像辨识和其他高阶能力都是透过神经处理器实现的，神经处理器是人工智慧演算法、深度学习模型和机器学习任务的大脑。数位来源、物联网设备和其他来源创建的资料呈指数级增长，推动了神经处理器的需求。处理器对于巨量资料分析和即时资料处理应用至关重要，因为它们旨在处理大量资料并执行复杂的计算。

边缘运算架构变得越来越普遍，特别是在物联网部署中，其中人工智慧处理发生在距离资料来源或端点设备更近的地方。对于边缘人工智慧应用，具有低功耗和强大运算能力的神经处理器非常适合。它允许即时资料处理、边缘人工智慧推理、降低延迟并提高物联网生态系统的效率。

神经处理器的需求在一定程度上是由边缘人工智慧设定的成长所推动的。神经处理器被云端服务供应商和AI服务平台用来为开发者和企业提供AI服务和解决方案。透过使用神经处理器，聊天机器人、情绪分析、推荐引擎、语音识别、语言翻译和资料分析等基于云端的人工智慧应用变得更加高效、可扩展且经济实惠。

开发成本高

作为新进者，特别是资金有限的小型企业或新创企业，高昂的开发成本为进入者设置了障碍。因此，市场竞争空间较小，可能导致市场份额集中于成熟企业，以及产品供应的创新和多样性减少。由于支出过高，旨在开发神经处理技术的研究与开发（R&D）计画无法获得资助。这可能会导致引入新功能或增强功能的延迟、创新週期更长以及产品缺乏特色。

为了收回大量的开发支出，製造商将不得不提高神经处理器的价格。在价格敏感的市场群体中，这可能会降低产品的竞争力并阻碍其市场渗透，特别是在新兴经济体或经济产业。企业必须投入大量的财力、人力资本和时间来开发神经处理器。组织的整体成长和竞争力都会受到这种资源分配的影响，这可能会使他们远离其他关键领域，例如客户服务、行销、销售和生态系统合作伙伴关係。

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