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市场调查报告书
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1444734

神经形态晶片：市场占有率分析、产业趋势与统计、成长预测（2024-2029）

Neuromorphic Chip - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2024 - 2029)
出版日期: | 出版商: Mordor Intelligence | 英文 151 Pages | 商品交期: 2-3个工作天内

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简介目录

神经形态晶片市场规模预计到 2024 年为 1.6 亿美元，预计到 2029 年将达到 58.3 亿美元，在预测期内（2024-2029 年）市场规模将增加 1,047 亿美元，复合年增长率为 %。

神经形态晶片 - 市场

主要亮点

  • 生物辨识和语音辨识的日益普及正在推动智慧型手机中对神经形态晶片的需求。这些晶片用于处理云端的语音资料并将其发送回您的手机。此外，虽然人工智慧（AI）需要更多的运算能力，但低能耗的神经拟态运算意味着目前在云端运行的应用程式未来可以显着减少行动电话电池电量，并可以显着推广直接在智慧型手机上运转而不消耗手机电量。 。
  • 神经形态晶片是特定的受大脑启发的 ASIC，可实现脉衝神经网路 (SNN)。目标是达到平均数十瓦的大规模平行大脑处理能力。记忆体和处理单元处于单一抽像中（记忆体内运算）。
  • 这在复杂环境中提供了动态的、自我可程式设计的行为。神经形态硬体不是传统的位元精确计算，而是产生一种机率计算模型，该模型与大脑的高机率性质一样简单、可靠、稳健且资料高效。神经形态硬体肯定比精确计算更适合认知应用。
  • 在接下来的十年中，神经形态计算将改变广泛的科学和非科学应用的性质和功能。其中行动应用程式越来越需要强大的处理能力和功能。
  • 神经形态晶片的设计遵循生物神经系统各部分建模的目标。目的是重现其运算能力，特别是有效解决认知和知觉任务的能力。为了实现这一目标，我们需要对神经元和突触连接数量足够复杂的网路进行建模。大脑及其学习和适应特定问题的能力仍然是基础神经科学研究的主题。
  • COVID-19感染疾病对医疗业务市场产生了积极影响。包括 IBM、惠普和高通在内的多家市场领导已在世界各地的多家医院和诊所部署了其神经拟态运算解决方案。他们的技术的计算能力使他们能够缓解典型医院生态系统中的各种困难。由于大流行，资本设备产业蓬勃发展，对下一代电子产品的需求强劲。

神经形态晶片市场趋势

消费性电子领域占据主要市场占有率

  • 消费性电子产业认识到神经拟态运算是一种很有前途的工具，可以实现高效能运算和超低功耗，从而实现这些目标。例如，Alexa 和 Siri 等人工智慧服务依赖于使用互联网的云端运算来解析和回应语音命令和问题。神经形态晶片具有无需互联网连接即可智慧运行各种感测器和设备的潜力。
  • 智慧型手机有望成为神经形态运算引入的催化剂。某些操作（例如生物识别）是电力和资料密集型的。例如，透过语音辨识，语音资料在云端进行处理，然后发送回您的手机。
  • 穿戴式装置是一项快速发展的技术，对个人医疗保健产生重大影响，无论是经济上还是社会上。由于感测器在普及网路中的普及，功耗、处理速度和系统适应性对于智慧穿戴式装置的未来至关重要。而且，人工智慧领域进一步增加了智慧穿戴感测系统的潜力。新的高效能係统和智慧应用需要更复杂的功能，并需要感官单元来准确说明实体物件。
  • 此外，穿戴式装置数量的增加可能会进一步推动市场成长。例如，根据思科系统公司的数据，2022 年连网穿戴装置数量将达到 11.05 亿台，而前一年为 9.29 亿台。
  • 人们对神经形态工程日益增长的兴趣意味着硬体尖峰神经网路被认为是未来的关键技术，在边缘运算和穿戴式装置等关键应用中具有巨大潜力。

北美在预测期内将维持主要份额

  • 北美是英特尔公司和IBM公司等主要市场供应商的所在地。由于政府措施和投资活动等因素，该地区的神经形态晶片市场正在成长。
  • 北美市场成长的关键因素之一是政府机构对神经形态计算的兴趣。
  • 例如，2022 年 9 月，美国能源部 (DOE) 宣布为 22 个研究计划提供 1,500 万美元资金，以推进神经形态计算。美国能源部的倡议将支持类脑神经形态运算的硬体和软体开发。
  • 同时，加拿大政府对人工智慧技术的关注预计将为神经形态运算在未来几年创造成长空间。例如，2022年6月，加拿大创新、科学与工业部宣布启动泛加拿大人工智慧战略第二阶段。该战略第二阶段的基础是 2021 年预算中 4.43 亿美元的投资。
  • 多个研究计划正在合作推进神经形态技术。例如，2022年8月，美国芝加哥大学普利兹克分子工程学院（PME）开发出一种灵活可拉伸的神经形态计算晶片，可以模仿人脑并处理资讯。该设备旨在改变健康资料的处理方式。
  • 基于人工智慧的晶片在加拿大不断成长，这也推动了神经形态晶片市场的发展。例如，2021年5月，加拿大新兴企业Tenstorrent宣布筹集2亿美元，并获得独角兽地位。该公司原计划在 2022 年上半年提供用于实际应用的 AI 晶片。
  • 各国国防费用的增加预计也将推动北美对神经拟态计算的需求。

神经形态晶片产业概况

神经形态晶片市场包括大型半导体供应商、架构开发新兴企业和具有显着产生收入能力的大学。市场正在整合，供应商越来越多地在研发和协作活动上投入资金，以获得技术力并使市场商业化，市场竞争日益激烈。

儘管神经形态晶片仍处于开发的早期阶段，但市场相关人员的专利申请活动正在引起主要半导体公司、研发中心和大学的兴趣，预计未来敌对行动将会加剧。

Edge Impulse 于 2022 年 8 月发布。这使得开发人员能够在低程式码环境中创建基于真实感测器资料训练的企业级机器学习演算法。这些经过训练的演算法可以量化、最佳化并转换为与 BrainChip Akida 设备相容的可部署尖峰神经网路 (SNN)。透过利用整合到平台中的 BrainChip MetaTF 模型部署区块，此功能可用于新的和现有的 Edge Impulse计划。此部署区块允许免费层和企业开发人员使用者在部署到 BrainChip Akida 开发套件之前为实际用例设计和评估神经形态模型。

2022年4月，SynSense宣布与BMW合作，推进神经形态晶片与智慧驾驶座的融合。这是SynSense类脑技术融入智慧驾驶座的第一步。与 BMW 的神经拟态技术合作将重点关注 SynSense 的动态视觉智慧 SoC-Speck，它将 SynSense 的低功耗 SNN 视觉处理器和基于事件的感测器整合在单一晶片上。

其他福利

  • Excel 格式的市场预测 (ME) 表
  • 3 个月分析师支持

目录

第一章简介

  • 研究假设和市场定义
  • 调查范围

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
  • 业界亮点－波特五力
    • 供应商的议价能力
    • 买方议价能力
    • 新进入者的威胁
    • 替代产品的威胁
    • 竞争公司之间的敌意强度
  • 产业价值链分析
  • 神经形态晶片的新用例
  • 评估 COVID-19 对市场的影响

第五章市场洞察

  • 市场驱动因素
    • 基于人工智慧的微晶片的需求增加
    • 神经可塑性概念与电子学结合的新趋势
  • 市场挑战
    • 硬体设计对高精度和复杂性的需求

第六章 全球深度学习市场分析

  • 目前的市场状况
  • 全球深度学习市场区隔
    • 按类型
      • CPU
      • GPU
      • FPGA
      • ASIC
      • SoC加速器
  • 涵盖深度学习软体和服务业的当前趋势
  • 投资场景
  • 主要硬体供应商列表
  • 未来市场展望

第七章市场区隔

  • 按最终用户产业
    • 金融服务及网路安全
    • 汽车（ADAS/自动驾驶汽车）
    • 工业（物联网生态系、监控、机器人）
    • 家用电器
    • 其他最终用户产业（医疗、航太、国防等）
  • 按地区
    • 北美洲
    • 欧洲
    • 亚太地区
    • 世界其他地区

第八章 竞争形势

  • 公司简介
    • Intel Corporation
    • SK Hynix Inc.
    • IBM Corporation
    • Samsung Electronics Co. Ltd
    • GrAI Matter Labs
    • Nepes Corporation
    • General Vision Inc.
    • Gyrfalcon Technology Inc.
    • BrainChip Holdings Ltd
    • Vicarious FPC Inc.
    • SynSense AG

第九章投资分析

第10章市场的未来

简介目录
Product Code: 54455

The Neuromorphic Chip Market size is estimated at USD 0.16 billion in 2024, and is expected to reach USD 5.83 billion by 2029, growing at a CAGR of 104.70% during the forecast period (2024-2029).

Neuromorphic Chip - Market

Key Highlights

  • 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. In addition, Artificial Intelligence (AI) requires more computing power, but low-energy neuromorphic computing could significantly push applications that run presently in the cloud to run directly in the smartphone in the future without substantially draining the phone battery.
  • Neuromorphic is a specific brain-inspired ASIC that implements the Spiked Neural Networks (SNNs). It has an object to reach the massively parallel brain processing ability in tens of watts on average. The memory and the processing units are in single abstraction (in-memory computing).
  • This leads to the advantage of dynamic, self-programmable behavior in complex environments. Instead of traditional bit-precise computing, neuromorphic hardware leads to the probabilistic models of simple, reliable, robust, and data-efficient computing as the brain's highly stochastic nature. Neuromorphic hardware certainly suits more cognitive applications than precise computing.
  • During the next decade, neuromorphic computing will transform the nature and functionalities of a wide range of scientific and non-scientific applications. Some of them include mobile applications that are increasingly demanding powerful processing capacities and abilities.
  • The design of neuromorphic chips follows the goal of modeling parts of the biological nervous system. The aim is to reproduce its computational functionality and especially its ability to solve cognitive and perceptual tasks efficiently. Achieving this requires modeling networks of sufficient complexity regarding the number of neurons and synaptic connections. The brain and its ability to learn and adapt to specific problems are still subject to basic neuroscientific research.
  • The COVID-19 pandemic had a favorable influence on the medical business market. Several market leaders, including IBM, Hewlett Packard, and Qualcomm, pushed their neuromorphic computing solutions into several hospitals and clinics worldwide. Their technologies' computational skills were able to reduce various difficulties inside a normal hospital ecosystem. The pandemic kept the capital equipment sector humming with a strong demand for next-generation electronics.

Neuromorphic Chip Market Trends

Consumer Electronics Segment Holds Significant Market Share

  • The consumer electronics industry identifies neuromorphic computing as a promising tool for enabling high-performance computing and ultra-low power consumption to achieve these goals. For instance, AI services, such as Alexa and Siri, rely on cloud computing with the internet to parse and respond to spoken commands and questions. Neuromorphic chips have the potential to allow several varieties of sensors and devices to perform intelligently without requiring an internet connection.
  • Smartphones are expected to be the trigger for the introduction of neuromorphic computing. Several operations, such as biometrics, are power-hungry and data-intensive. For instance, in speech recognition, audio data is processed in the cloud and then returned to the phone.
  • Wearable devices are a fast-growing technology with a considerable impact on personal healthcare for both the economy and society. Due to widespread sensors in pervasive and distributed networks, power consumption, processing speed, and system adaptation are vital in the future of smart wearable devices. Additionally, the field of artificial intelligence further boosts the possibility of smart wearable sensory systems. The emerging high-performance systems and intelligent applications need more complexity and demand sensory units to describe the physical object accurately.
  • Moreover, increasing the number of wearable devices may further drive market growth. For instance, according to Cisco Systems, the number of connected wearable devices reached 1,105 million in 2022 compared to 929 million in the previous year.
  • The increasing interest in neuromorphic engineering shows that hardware-spiking neural networks are considered a critical future technology with high potential in crucial applications, such as edge computing and wearable devices.

North America to Hold Major Share over the Forecast Period

  • North America is home to some of the major market vendors, such as Intel Corporation and IBM Corporation. The market for neuromorphic chips is growing in the region due to factors such as government initiatives, investment activities, and others.
  • One of the significant factors behind the growth of the market in North America is the interest shown by government bodies toward neuromorphic computing.
  • For instance, in September 2022, the Department of Energy (DOE) announced USD 15 million in funding for 22 research projects to advance neuromorphic computing. The initiative by DOE supports the development of hardware and software for brain-inspired neuromorphic computing.
  • On the other hand, the government of Canada is focusing on artificial intelligence technology, which is also expected to create a scope for growth in neuromorphic computing over the coming years. For instance, in June 2022, the Canadian Ministry of Innovation, Science, and Industry announced the start of the second phase of the Pan-Canadian Artificial Intelligence Strategy. The second phase of the strategy is backed by a USD 443 million investment in Budget 2021.
  • Several research projects are attracting collaborations for advancements in neuromorphic technology. For instance, in August 2022, the Pritzker School of Molecular Engineering (PME) at the University of Chicago in the United States developed a flexible, stretchable neuromorphic computing chip that processes information by mimicking the human brain. The device intends to alter the way health data is processed.
  • There has been growth in AI-based chips in Canada, which is also driving the neuromorphic chips market. For instance, in May 2021, Canadian startup Tenstorrent announced that it had raised USD 200 million and achieved unicorn status. The company had planned to deliver its AI chip for real-world applications in the first half of 2022.
  • The increasing defense expenditure of various countries is also expected to drive the demand for neuromorphic computing in North America.

Neuromorphic Chip Industry Overview

The neuromorphic chip market has large-scale semiconductor vendors that command significant revenue generation capabilities, architecture-development start-ups, and universities. The market is consolidated, and vendors are increasingly spending on R&D and collaboration activities to gain technological capabilities and commercialize the market, making the market less competitive.

Despite neuromorphic chips being at an early stage of development, the patent filing activity by players in the market is gaining interest across key semiconductor companies, R&D centers, and universities, and competitive rivalry is poised to increase in the future.

In August 2022, Edge Impulse was launched, which enables developers to create enterprise-grade ML algorithms trained on real-world sensor data in a low-code environment. These trained algorithms can be quantified, optimized, and turned into Spiking Neural Networks (SNN) that are compatible with and deployable with BrainChip Akida devices. This functionality is available for new and existing Edge Impulse projects by utilizing the platform's integrated BrainChip MetaTF model deployment block. This deployment block allows free-tier and enterprise developer users to design and evaluate neuromorphic models for real-world use cases before deploying them on BrainChip Akida development kits.

In April 2022, SynSense announced a collaboration with BMW to advance the integration of neuromorphic chips and smart cockpits. This is the first step in integrating SynSense's brain-like technology into smart cockpits. This neuromorphic technology collaboration with BMW will focus on SynSense's dynamic visual intelligence SoC-Speck, which combines SynSense's low-power SNN vision processor with an event-based sensor on a single chip.

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 Five Forces
    • 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 Emerging Use Cases for Neuromorphic Chips
  • 4.5 Assessment of the Impact of COVID-19 on the Market

5 MARKET INSIGHTS

  • 5.1 Market Drivers
    • 5.1.1 Increasing Demand for Artificial Intelligence-based Microchips
    • 5.1.2 Emerging Trend of Combining the Concept of Neuroplasticity with Electronics
  • 5.2 Market Challenges
    • 5.2.1 Need for High Level of Precision and Complexity in Hardware Design

6 GLOBAL DEEP LEARNING MARKET ANALYSIS

  • 6.1 Current market scenario
  • 6.2 Global Deep Learning Market Segmentation
    • 6.2.1 By Type
      • 6.2.1.1 CPU
      • 6.2.1.2 GPU
      • 6.2.1.3 FPGA
      • 6.2.1.4 ASIC
      • 6.2.1.5 SoC Accelerators
  • 6.3 Coverage on the Current Trends in the Deep Learning Software and Service industry
  • 6.4 Investment Scenario
  • 6.5 List of Major Hardware Vendors
  • 6.6 Future of the Market

7 MARKET SEGMENTATION

  • 7.1 By End-User Industry
    • 7.1.1 Financial Services and Cybersecurity
    • 7.1.2 Automotive (ADAS/Autonomous Vehicles)
    • 7.1.3 Industrial (IoT Ecosystem, Surveillance, and Robotics)
    • 7.1.4 Consumer Electronics
    • 7.1.5 Other End-user Industries (Medical, Space, Defense, Etc.)
  • 7.2 By Geography
    • 7.2.1 North America
    • 7.2.2 Europe
    • 7.2.3 Asia Pacific
    • 7.2.4 Rest of the World

8 COMPETITIVE LANDSCAPE

  • 8.1 Company Profiles
    • 8.1.1 Intel Corporation
    • 8.1.2 SK Hynix Inc.
    • 8.1.3 IBM Corporation
    • 8.1.4 Samsung Electronics Co. Ltd
    • 8.1.5 GrAI Matter Labs
    • 8.1.6 Nepes Corporation
    • 8.1.7 General Vision Inc.
    • 8.1.8 Gyrfalcon Technology Inc.
    • 8.1.9 BrainChip Holdings Ltd
    • 8.1.10 Vicarious FPC Inc.
    • 8.1.11 SynSense AG

9 INVESTMENT ANALYSIS

10 FUTURE OF THE MARKET