欧洲神经型态晶片：市场占有率分析、产业趋势与统计、成长预测（2025-2030 年）

预计预测期内欧洲神经型态晶片市场复合年增长率将达到 50.2%。

主要亮点

• 随着智慧技术的出现，智慧感测器被应用于许多终端消费产业，包括汽车、电子和医疗。儘管在研发方面投入了大量资金并且硬体设计的复杂性不断增加，但神经型态晶片市场的成长仍然受到发展速度缓慢的阻碍。
• 神经型态晶片可部署于该地区的广泛终端客户，包括医疗、海军和国防领域。目前的深度掌握策略和相关硬体面临许多障碍，包括摩尔定律的经济性。
• 同时，市场需要增加即时语音辨识和翻译性能、即时视讯理解以及机器人和汽车的即时视图。一些程式需要结合感知和计算的额外智慧。
• 此外，生物识别和语音辨识的使用日益增多也推动了智慧型手机对神经型态晶片的需求。这些晶片用于处理云端的语音资料并将其发送回行动电话。此外，人工智慧（AI）需要更多的运算能力。不过，低能耗神经形态运算可以显着促进目前驻留在云端的应用程式未来直接在智慧型手机上运行，而不会显着消耗智慧型手机的电池。
• 推动神经型态应用的一些突出特点包括低功耗、机率计算、模式识别、容错、更快的计算和可扩展性。在晶片上模拟神经元应该可以改善流程并使商业决策更具成本效益和可靠性。
• 新冠肺炎疫情在全球爆发，造成了前所未有的局面，造成大量人员死亡，并有社区蔓延的风险。这使得采取紧急措施变得必要，即透过各种医学上已证实的方法，如胸部电脑断层扫描（CT）和胸部X光，儘早发现疾病，并防止病毒在全部区域传播。开发深度学习模型来分析这些放射影像是基于电脑的医学影像分析中的一种众所周知的技术。此外，随着各国解除限制、经济恢復正常，市场也正在获得动力。预测期内，神经型态晶片的需求预计会大幅成长。

欧洲神经型态晶片市场趋势

基于人工智慧的晶片有望推动市场成长

• 人工智慧（AI）正在吸引各大企业的巨额投资，晶片市场的兴趣和关注度日益提升。许多应用程式已经被最终用户采用，并且预计不久的将来还会出现许多新兴应用程式。
• 人工智慧加速器在超越CPU运算极限的领域中占据领先地位。可用的 AI 加速器包括 GPU、专用积体电路 (ASIC) 和现场可程式闸阵列 (FPGA)。 GPU拥有大量平行处理核心，在处理AI训练和推理方面具有显着优势。
• 神经型态晶片在并行性、能源效率和性能方面有望成为一个引人注目的选择。这些晶片可以即时处理AI推理和训练。此外，还可以透过神经型态晶片进行边缘训练。但该学习方法的准确性仍有待提升。
• 随着感测、运算和记忆领域的追求和需求的融合，神经形态硬体正走出实验室。已经建立了合资企业并启动了为期10年的研究任务，其中包括欧盟的「人脑」。
• 神经形态认知和运算可以解决人工智慧目前面临的许多问题，同时也为未来几十年开闢新的应用。凭藉其研究和创新生态系统，欧洲有能力发展其科学文化并利用技术机会。

英国神经型态晶片将创下成长纪录

• 由于政府计划和供应商的投资，欧洲，特别是英国的神经型态晶片预计也将成长。几个长期研究计划正在邀请合作来推进神经型态技术。此外，义大利、德国、法国、葡萄牙等国家也正致力于提升神经型态晶片在各个业务领域的成长。
• 此外，由欧盟研究与创新计画H2020资助的NeuronN研究计划，以「节能的仿生设备加速实现类脑运算」为核心主题，汇集了欧洲知名研究和学术机构。计划预计工期36个月（2020年1月至2022年12月）。
• 该地区的本地供应商也正致力于透过市场上各类创业投资的资金筹措来开发神经型态晶片。预计此类投资将在技术创新方面塑造市场。
• 此外，英国曼彻斯特大学的先进处理器技术小组正在研发一款名为 SpiNNaker 的低阶超级电脑。它代表脉衝神经网路架构。这可以刺激所谓的皮质微电路，进而刺激人类的精神皮层并有助于识别老年痴呆症症等复杂疾病。
• Chip AI 和英国思克莱德大学的一项研究发现，在当今资料主导的社会中，处理大量资料的能力至关重要。模式辨识和影像分类等重要任务非常适合人工神经网路 (ANN)。它受到旨在物理实现大脑和 ANN 的神经形态计算方法的启发。

欧洲神经型态晶片产业概况

欧洲神经型态市场较分散，主要企业包括： Brain Corporation、英特尔公司、高通、IBM Corporation、General Vision Inc.、洛克希德马丁公司、Innatera Nanosystems BV 等。研究下列的。

• 2022 年 4 月 - BrainChipHoldings Ltd 和 nViso SA 宣布合作，以满足机器人、移动/汽车领域对超低功耗技术高水准 AI 性能的需求。第一阶段，NVISO 针对社群机器人和车载监控系统的 AI 解决方案将在 BrainChip 的 AkidaTM 处理器上实现。
• 2022 年 3 月 - Brainchip 是一家超低功耗神经形态 AI 晶片和 IP 的商业製造商，该公司正在优化整个欧洲的本地市场销售，以扩大其 Akidaneuromorphic 运算平台的商业性覆盖范围。该公司宣布已收购SalesLink ，一家欧洲技术解决方案提供商。

其他福利

• Excel 格式的市场预测 (ME) 表
• 3 个月的分析师支持

目录

第 1 章 简介

• 研究假设和市场定义
• 研究范围

第二章调查方法

第三章执行摘要

第四章 市场洞察

• 市场概况
• 产业吸引力－波特五力分析
  • 供应商的议价能力
  • 买家的议价能力
  • 新进入者的威胁
  • 替代品的威胁
  • 竞争对手之间的竞争强度
• 产业价值链分析
• COVID-19 市场影响评估
• 超级电脑处理技术简介

第五章 市场动态

• 市场驱动因素
  • 人工智慧的发展
  • 积体电路小型化
  • 提高感测器性能
• 市场限制
  • 儘管在研发上投入了大量资金，但应用开发的速度却很慢
  • 硬体设计复杂性

第六章 市场细分

• 按应用
  • 影像识别
  • 讯号处理
  • 资料处理
  • 其他的
• 按最终用户
  • 航太和国防
  • 汽车与运输
  • 工业自动化
  • 卫生保健
  • 消费品产业
  • 其他的
• 按国家
  • 英国
  • 德国
  • 法国
  • 其他欧洲国家

第七章 竞争格局

• 公司简介
  • Intel Corporation
  • Qualcomm Technologies, Inc.
  • IBM Corporation
  • Samsung
  • HRL Laboratories
  • General Vision, Inc.
  • Lockheed Martin Corporation
  • Innatera Nanosystems BV
  • Brain Corporation
  • Vicarious FPC Inc.

第八章投资分析

第九章 市场机会与未来趋势

The Europe Neuromorphic Chip Market is expected to register a CAGR of 50.2% during the forecast period.

Key Highlights

• With the appearance of intelligent technology, smart sensors are getting used in many end-consumer industries like automotive, electronics, and medical. The increase of the neuromorphic chip marketplace is hindered because of the gradual tempo of development, notwithstanding heavy R&D investments and rising complexities of hardware design.
• Implementing neuromorphic chips throughout extensive end-customers, including medical, navy, protection, etc., is possible in this region. Current deep-mastering strategies and related hardware face numerous hurdles, including the economics of Moore's Law, which makes it appreciably hard for a start to compete withinside the AI space, restricting competition.
• Meanwhile, the market needs extra real-time speech popularity and translation performance, real-time video understanding, and real-time view for robots and cars. Several programs require additional intelligence that mixes sensing and computing.
• Moreover, the increasing use of biometrics and in-speech recognition drives the demand for neuromorphic chips in smartphones. These chips are used to process audio data in the cloud and then return it to the phone. Additionally, Artificial Intelligence (AI) requires more computing power. Still, low-energy neuromorphic computing could significantly push applications presently in the cloud to run directly in the smartphone in the future without substantially draining the phone battery.
• The prominent features driving the adoption of neuromorphic chipsets include low power consumption, stochastic operations, pattern recognition, fault tolerance, faster computation, and scalability. Emulating neurons on a chip should improve processes and make business decisions cost-effective and reliable.
• The outbreak of COVID-19 has created unprecedented circumstances resulting in many deaths and risk of community spreading throughout the world. This created a need for desperate measures to detect the disease at an early stage via various medically proven methods like chest computed tomography (CT) scan, chest X-Ray, etc., to prevent the virus from spreading across the community. Developing deep learning models for analyzing these radiological images is a well-known methodology in computer-based medical image analysis. Further, the market has gained traction as the countries are removing restrictions and things are returning to normal. The demand for neuromorphic chips is anticipated to grow significantly over the forecasted period.

Europe Neuromorphic Chip Market Trends

AI-based Chips are Expected to Drive the Market Growth

• Artificial Intelligence (AI) is witnessing significant corporate investment, and the chips market is receiving increasing interest and attention. End-users have already adopted many applications, and numerous emerging applications are expected to happen in the short term.
• AI accelerators are leading the region because of the computing limitations of CPUs. Available AI accelerators are GPUs, Application-Specific Integrated Circuits (ASICs), and Field-Programmable Gate Arrays (FPGAs). GPUs have many parallel processing cores, which gives them a significant advantage for processing AI training and inference.
• Neuromorphic chips are poised to be the prominent option concerning parallelism, energy efficiency, and performance. They can handle both AI inference and training in real time. Moreover, edge training is possible through neuromorphic chips. However, learning methodologies should improve their accuracy.
• Neuromorphic hardware is transferring out of the studies lab with a convergence of pursuits and desires from the sensing, computing, and reminiscence fields. Joint ventures are being formed, and decade-length investigation tasks, including the European Union's Human Brain, are being launched.
• Neuromorphic recognition and computation can solve many of AI's current problems while opening up new applications for decades. With an ecosystem of research and innovation, Europe is well-positioned to advance a culture of science and capitalize on technological opportunities.

United Kingdom to record growth in Neuromorphic Chips

• The European region, mainly the United Kingdom, is also expected to witness growth in neuromorphic chips due to government projects, investments from vendors, etc. Several long-term research projects are attracting collaborations for advancements in neuromorphic technology. Also, Italy, Germany, France, Portugal, and other countries are focusing on improving the growth of neuromorphic chips in various business areas.
• Also, the NeurONN research project funded by H2020's EU research and innovation program with a core subject, "Energy-efficient bio-inspired devices accelerate the route to brain-like computing," brought together some prominent European research and academic institutions. The project is planned for 36 months (January 2020 - December 2022).
• Local vendors in the region also focus on developing neuromorphic chips through funding from various venture capitalists in the market. Such investments are expected to shape the market in terms of innovation.
• Further, the United Kingdom's Advanced Processor Technologies Group at the University of Manchester runs on a low-grade supercomputer called SpiNNaker. It stands for Spiking Neural Network Architecture. It is assumed to stimulate so-called cortical microcircuits, subsequently the human mind cortex, and assist us in recognizing complicated illnesses like Alzheimer's.
• A study by Chip AI and Strathclyde University in the United Kingdom found that the ability to process large amounts of data in today's data-driven world is critical. Critical tasks such as pattern recognition and image classification are well suited for artificial neural networks (ANN). They are inspired by neuromorphic computing approaches that target the physical implementation of brains and ANNs.

Europe Neuromorphic Chip Industry Overview

The Europe neuromorphic Chip Market is moderately fragmented, with key players such as Brain Corporation, Intel Corporation, Qualcomm, IBM Corporation, General Vision Inc., Lockheed Martin Corporation, Innatera Nanosystems BV, and more. These companies are investing and innovating new products to expand the market in various applications.

• April 2022 - BrainChipHoldings Ltd and nViso SA announced a collaboration to address the need for high levels of AI performance with ultra-low power technologies in robotics and mobility/automotive. The first step will be implementing NVISO's AI solutions for Social Robots and In-cabin Monitoring Systems on BrainChip's AkidaTMprocessors.
• March 2022 - Brainchip, a company involved in the commercial production of ultra-low power neuromorphic AI chips and IP, announced that it has SalesLink, a European technology solutions provider, to optimize local market sales throughout the region to expand the commercial reach of its Akidaneuromorphic computing platform.

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 Study Assumptions and Market Definition
• 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET INSIGHTS

• 4.1 Market Overview
• 4.2 Industry Attractiveness - Porter's Five Forces Analysis
  • 4.2.1 Bargaining Power of Suppliers
  • 4.2.2 Bargaining Power of Buyers
  • 4.2.3 Threat of New Entrants
  • 4.2.4 Threat of Substitute Products
  • 4.2.5 Intensity of Competitive Rivalry
• 4.3 Industry Value Chain Analysis
• 4.4 Assessment of the Impact of COVID-19 on the Market
• 4.5 Super Computer Processing Technology Snapshot

5 MARKET DYNAMICS

• 5.1 Market Drivers
  • 5.1.1 Development of Artificial Intelligence
  • 5.1.2 Miniaturization of Ics
  • 5.1.3 Growth in Demand for Smarter Sensors
• 5.2 Market Restraints
  • 5.2.1 Slow Pace in Development of Applications Despite Heavy investments in R&D
  • 5.2.2 Commplexities in Hardware Designing

6 MARKET SEGMENTATION

• 6.1 Application
  • 6.1.1 Image Recognition
  • 6.1.2 Signal Processing
  • 6.1.3 Data Processing
  • 6.1.4 Others
• 6.2 End-User
  • 6.2.1 Aerospace and Defence
  • 6.2.2 Automotive and Transportation
  • 6.2.3 Industrial Automation
  • 6.2.4 Health Care
  • 6.2.5 Consumer Industry
  • 6.2.6 Others
• 6.3 Country
  • 6.3.1 United Kingdom
  • 6.3.2 Germany
  • 6.3.3 France
  • 6.3.4 Rest of Europe

7 COMPETITIVE LANDSCAPE

• 7.1 Company Profiles
  • 7.1.1 Intel Corporation
  • 7.1.2 Qualcomm Technologies, Inc.
  • 7.1.3 IBM Corporation
  • 7.1.4 Samsung
  • 7.1.5 HRL Laboratories
  • 7.1.6 General Vision, Inc.
  • 7.1.7 Lockheed Martin Corporation
  • 7.1.8 Innatera Nanosystems B.V.
  • 7.1.9 Brain Corporation
  • 7.1.10 Vicarious FPC Inc.

8 INVESTMENT ANALYSIS

9 MARKET OPPORTUNITIES AND FUTURE TRENDS