세계의 AI 칩 시장 예측(-2029년) : 컴퓨팅별, 메모리별, 네트워크별, 기술별, 기능별, 최종사용자별, 지역별
AI Chip Market by Offerings (GPU, CPU, FPGA, NPU, TPU, Trainium, Inferentia, T-head, Athena ASIC, MTIA, LPU, Memory (DRAM (HBM, DDR)), Network (NIC/Network Adapters, Interconnects)), Function (Training, Inference) & Region - Global Forecast to 2029
상품코드:1558152
리서치사:MarketsandMarkets
발행일:2024년 08월
페이지 정보:영문 359 Pages
라이선스 & 가격 (부가세 별도)
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한글목차
세계의 AI 칩 시장 규모는 2024년 1,232억 달러에서 성장하며, 2029년에는 3,115억 8,000만 달러에 달할 것으로 보이며, 2024-2029년 CAGR은 20.4%로 성장할 것으로 예측됩니다.
조사 범위
조사 대상년
2020-2029년
기준년
2023년
예측 기간
2024-2029년
검토 단위
금액( 100만 달러)
부문별
컴퓨팅별, 메모리별, 네트워크별, 기술별, 기능별, 최종사용자별, 지역별
대상 지역
북미, 유럽, 아시아태평양, 기타 지역
AI 칩 시장은 머신러닝과 딥러닝 알고리즘의 채택이 증가함에 따라 성장할 것으로 예상되며, AI 서버의 출하량 증가는 AI 기능을 지원하는 칩에 대한 수요를 증가시킬 것으로 보입니다. 또한 자율주행 자동차의 새로운 동향은 실시간 의사 결정에 사용되는 AI 칩 시장 성장을 촉진할 것으로 예상됩니다.
신경처리장비(NPU) 부문은 2024-2029년 AI 칩 시장에서 높은 성장률을 보일 것으로 예상됩니다. 이러한 시장 성장의 배경에는 고급 스마트폰, AI PC 및 노트북의 채택이 증가하면서 엣지에 전용 AI 기능이 필요해졌고, NPU는 고급 AI 이미지 처리 및 자연어 처리와 같은 AI 기반 작업을 수행하기 위해 신경망 처리 속도를 높이는 데 도움이 될 것입니다. 속도를 높이는 데 도움이 됩니다. 시장에서 경쟁력을 유지하기 위해 시장 기업은 하이엔드 NPU 솔루션 개발에 광범위하게 집중하고 있습니다.
AI 칩 시장의 머신러닝 분야는 예측 기간 중 높은 성장세를 보일 것으로 예상되며, AI 칩은 학습 및 추론과 같은 머신러닝 작업에 최적화되어 있으며, 대규모 데이터세트를 실행 및 처리하고 예측 분석을 가능하게 하며 실시간 의사결정을 지원하는 데 중요한 역할을 합니다. 이 카테고리의 AI 칩은 자율 시스템내 머신러닝 모델의 유연성과 확장성, 그리고 개인화된 추천이 가장 큰 채택 요인으로 작용하고 있습니다. 이 AI 칩은 클라우드 서비스, 헬스케어, 금융, 자동차, 소매 등 다양한 분야에서 널리 사용되고 있으며, 각 기업은 머신러닝 기능을 지원하는 강력한 AI 칩을 개발하고 있습니다.
세계의 AI 칩 시장에 대해 조사했으며, 컴퓨팅별, 메모리별, 네트워크별, 기술별, 기능별, 최종사용자별, 지역별 동향 및 시장에 참여하는 기업의 개요 등을 정리하여 전해드립니다.
목차
제1장 서론
제2장 조사 방법
제3장 개요
제4장 주요 인사이트
제5장 시장 개요
서론
시장 역학
고객 비즈니스에 영향을 미치는 동향/혼란
가격 분석
밸류체인 분석
에코시스템 분석
투자와 자금조달 시나리오
기술 분석
서버 비용 구조/부품 표
AI 서버의 보급과 성장
클라우드 서비스 프로바이더(CSPS)별 데이터센터의 향후 도입
클라우드 서비스 프로바이더의 설비 투자
클라우드 서비스 프로바이더별 서버 조달, 2020-2029년
프로세서 벤치마킹
특허 분석
무역 분석
2024-2025년 주요 컨퍼런스와 이벤트
사례 연구 분석
규제 상황
Porter's Five Forces 분석
주요 이해관계자와 구입 기준
제6장 AI 칩 시장, 컴퓨팅별
서론
GPU
CPU
FPGA
NPU
TPU
DOJO, FSD
TRAINIUM, INFERENTIA
ATHENA ASIC
T-HEAD
MTIA
LPU
기타
제7장 AI 칩 시장, 메모리별
서론
DDR
HBM
제8장 AI 칩 시장, 네트워크별
서론
NIC/네트워크 어댑터
인터커넥트
제9장 AI 칩 시장, 기술별
서론
생성형 AI
기계학습
자연언어처리
컴퓨터 비전
제10장 AI 칩 시장, 기능별
서론
트레이닝
추론
제11장 AI 칩 시장, 최종사용자별
서론
소비자
데이터센터
정부기관
제12장 AI 칩 시장, 지역별
서론
북미
유럽
아시아태평양
기타 지역
제13장 경쟁 구도
서론
주요 참여 기업의 전략/비책, 2019-2024년
매출 분석, 2021-2023년
시장 점유율 분석, 2023년
기업 가치 평가와 재무 지표
브랜드/제품 비교
기업 평가 매트릭스 : 주요 참여 기업, 2023년
기업 평가 매트릭스 : 스타트업/중소기업, 2023년
경쟁 시나리오
제14장 기업 개요
주요 참여 기업
NVIDIA CORPORATION
ADVANCED MICRO DEVICES, INC.
INTEL CORPORATION
SK HYNIX INC.
SAMSUNG
MICRON TECHNOLOGY, INC.
APPLE INC.
QUALCOMM TECHNOLOGIES, INC.
HUAWEI TECHNOLOGIES CO., LTD.
GOOGLE
AMAZON WEB SERVICES, INC.
TESLA
MICROSOFT
META
T-HEAD
IMAGINATION TECHNOLOGIES
GRAPHCORE
CEREBRAS
기타 기업
MYTHIC
KALRAY
BLAIZE
GROQ, INC.
HAILO TECHNOLOGIES LTD
GREENWAVES TECHNOLOGIES
SIMA TECHNOLOGIES, INC.
KNERON, INC.
RAIN NEUROMORPHICS INC.
TENSTORRENT
SAMBANOVA SYSTEMS, INC.
TAALAS
SAPEON INC.
REBELLIONS INC.
RIVOS INC.
SHANGHAI BIREN TECHNOLOGY CO., LTD.
제15장 부록
KSA
영문 목차
영문목차
The AI Chip market is projected to grow from USD 123.2 billion in 2024 and is estimated to reach USD 311.58 billion by 2029; it is expected to grow at a CAGR of 20.4% from 2024 to 2029.
Scope of the Report
Years Considered for the Study
2020-2029
Base Year
2023
Forecast Period
2024-2029
Units Considered
Value (USD Million)
Segments
By Offerings, Memory, Network, Function & Region
Regions covered
North America, Europe, APAC, RoW
The market for AI chips is expected to grow due to increasing adoption of machine learning and deep learning algorithms. The increase in AI server shipments will boost the demand for chips supporting AI capabilities. Moreover, the emerging trend of autonomous vehicles is expected to boost the market for AI chips used for real-time decision making.
"The Neural Processing Unit (NPU) segment is projected to grow at a high rate during the forecast period."
The Neural Processing Unit (NPU) segment is projected grow at a high rate in the AI chip market from 2024 to 2029. The market growth is attributed to the increasing adoption of high-end smartphones and AI PCs and laptops which requires dedicated AI capabilities at the edge. The NPUs helps to accelerate the neural network processing to perform the AI-driven tasks including advanced AI image processing and natural language processing. Market players are extensively focusing on developing high-end NPU solutions to stay competitive in the market. For instance, in September 2023, Apple Inc. (US) launched the iPhone 15 Pro series, featuring the A17 Pro chip. The new AI processor is incorporated with a dedicated 16-core Neural Engine which has capabilities of performing 35 trillion operations per second (TOPS). Such significant product developments and launches are expected to amplify the adoption of NPUs in the market over the forecast timeframe.
"Machine Learning segment of the AI Chip market to witness high market share during the forecast period."
The machine learning segment in AI chip market is expected to grow at a high rate during the forecast period. AI chips are critical in running large datasets to process and enable predictive analytics, supporting real-time decision-making, as they are optimized to machine learning tasks such as training and inference. For this category of AI chips, the foremost drivers of adoption were flexibility and scalability of machine learning models within autonomous systems and personalized recommendations. This AI chip is widely used in many sectors-from cloud services and healthcare to finance, automotive, and retail-in which companies are developing powerful AI chips in support of machine learning capabilities, where business insights can be gained, the customer experience improved, and efficiency generally jacked. For instance, Google (US) announced Trillium in May 2024 as its sixth-generation TPU. It focuses on its cloud platform with an onboard accelerator for machine learning workload acceleration. Enterprises that have adopted TPUs widely bring machine learning power to predictive analytics, personalization, and operational efficiency. This represents increasing dependence on AI chips in this domain. As businesses seek to exploit the power of data for insight, efficiencies, and customer experience, demand is surging for machine learning capabilities.
"North America to hold a major market share of the AI chip market during the forecast period" North America took the largest market share for the AI chip market in 2023. The presence of prominent technology firms and data center operators are driving the AI chip market across North America region. The region hosts companies such as NVIDIA Corporation (US), Intel Corporation (US), Advanced Micro Devices, Inc. (AMD) (US), Google (US); and cloud service providers include Amazon Web Services, Inc. (AWS) (US), Microsoft Azure (US), and Google Cloud (US). For instance, in April 2024, Google (US) announced a USD 3 billion investment to expand their data centers across the US. These data centers are further backed by AI infrastructure to provide real-time services across the world. The region also hosts several startups set up in the area for providing AI chips for data centers, which include SAPEON Inc. (US), Tenstorrent (Canada), Taalas (Canada), Kneron, Inc. (US), SambaNova Systems, Inc. (US). North America has a well-established technological infrastructure that supports advanced AI research and development. There are very many modern data centers in this region, equipped with state-of-the-art AI hardware. They may include GPUs and TPUs, as well as specialized AI chips. The presence of large scale data centers and leading AI chip developers in the region are driving the market growth of AI chips.
Extensive primary interviews were conducted with key industry experts in the AI chip market to determine and verify the market size for various segments and subsegments gathered through secondary research. The break-up of primary participants for the report has been shown below:
The break-up of the profile of primary participants in the AI chip market:
By Company Type: Tier 1 - 45%, Tier 2 - 32%, and Tier 3 - 23%
By Designation: C-level - 30%, Director Level - 45%, Others- 25%
By Region: North America - 26%, Europe - 40%, Asia Pacific - 22%, ROW- 12%
The report profiles key players in the AI Chip market with their respective market ranking analysis. Prominent players profiled in this report are NVIDIA Corporation (US), Intel Corporation (US), Advanced Micro Devices, Inc. (US), Micron Technology, Inc. (US), Google (US), Samsung (South Korea), SK HYNIX INC. (South Korea), Qualcomm Technologies, Inc. (US), Huawei Technologies Co., Ltd. (China), Apple Inc. (US), Imagination Technologies (UK), Graphcore (UK), Cerebras (US).
Apart from this, Mythic (US), Kalray (France), Blaize (US), Groq, Inc. (US), HAILO TECHNOLOGIES LTD (Israel), GreenWaves Technologies (France), SiMa Technologies, Inc. (US), Kneron, Inc. (US), Rain Neuromorphics Inc. (US), Tenstorrent (Canada), SambaNova Systems, Inc. (US), Taalas (Canada), SAPEON Inc. (US), Rebellions Inc. (South Korea), Rivos Inc. (US), and Shanghai BiRen Technology Co., Ltd. (China) are among a few emerging companies in the AI chip market.
Research Coverage: This research report categorizes the AI Chip market on the basis of offerings, function, technology, end user, and region. The report describes the major drivers, restraints, challenges, and opportunities pertaining to the AI chip market and forecasts the same till 2029. Apart from these, the report also consists of leadership mapping and analysis of all the companies included in the AI chip ecosystem.
Key Benefits of Buying the Report The report will help the market leaders/new entrants in this market with information on the closest approximations of the revenue numbers for the overall AI chip market and the subsegments. This report will help stakeholders understand the competitive landscape and gain more insights to position their businesses better and to plan suitable go-to-market strategies. The report also helps stakeholders understand the pulse of the market and provides them with information on key market drivers, restraints, challenges, and opportunities.
The report provides insights on the following pointers:
Analysis of key drivers (increasing data traffic and need for high computing power, emerging trend of autonomous vehicles, growing adoption of industrial robots, rising focus on parallel computing in AI data centers, increasing adoption of machine learning and deep learning algorithms, increase in AI server shipments to boost the demand for AI chips), restraints (lack of AI hardware experts and skilled workforce, increasing power consumption), opportunities (surging demand for AI-based field programmable gate array (FPGA) technology, integration of AI-based solutions into defense systems, growing potential of AI-based tools in healthcare sector, planned investments in data centers by cloud service providers, rise of ASICs based on AI technology), and challenges (data privacy concerns associated with AI platforms, unreliability of AI algorithms, availability of limited structured data to develop efficient AI systems, supply chain disruptions) influencing the growth of the AI Chip market.
Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new product & service launches in the AI chip market.
Market Development: Comprehensive information about lucrative markets - the report analysis the AI chip market across various regions
Market Diversification: Exhaustive information about new products & services, untapped geographies, recent developments, and investments in the Ai Chip market.
Competitive Assessment: In-depth assessment of market shares, growth strategies and product offerings of leading players like NVIDIA Corporation (US), Intel Corporation (US), Advanced Micro Devices, Inc. (US), Micron Technology, Inc. (US), Google (US), among others in the AI Chip market.
TABLE OF CONTENTS
1 INTRODUCTION
1.1 STUDY OBJECTIVES
1.2 MARKET DEFINITION
1.3 STUDY SCOPE
1.3.1 MARKETS COVERED AND REGIONAL SCOPE
1.3.2 INCLUSIONS AND EXCLUSIONS
1.3.3 YEARS CONSIDERED
1.4 CURRENCY CONSIDERED
1.5 UNIT CONSIDERED
1.6 LIMITATIONS
1.7 STAKEHOLDERS
1.8 SUMMARY OF CHANGES
2 RESEARCH METHODOLOGY
2.1 RESEARCH DATA
2.1.1 SECONDARY AND PRIMARY RESEARCH
2.1.2 SECONDARY DATA
2.1.2.1 List of key secondary sources
2.1.2.2 Key data from secondary sources
2.1.3 PRIMARY DATA
2.1.3.1 List of primary interview participants
2.1.3.2 Breakdown of primaries
2.1.3.3 Key data from primary sources
2.1.3.4 Key industry insights
2.2 MARKET SIZE ESTIMATION METHODOLOGY
2.2.1 BOTTOM-UP APPROACH
2.2.1.1 Approach to arrive at market size using bottom-up analysis (demand side)
2.2.2 TOP-DOWN APPROACH
2.2.2.1 Approach to arrive at market size using top-down analysis (supply side)
2.3 DATA TRIANGULATION
2.4 RESEARCH ASSUMPTIONS
2.5 RISK ANALYSIS
2.6 RESEARCH LIMITATIONS
3 EXECUTIVE SUMMARY
4 PREMIUM INSIGHTS
4.1 ATTRACTIVE OPPORTUNITIES FOR PLAYERS IN AI CHIP MARKET
4.2 AI CHIP MARKET, BY COMPUTE
4.3 AI CHIP MARKET, BY MEMORY
4.4 AI CHIP MARKET, BY NETWORK
4.5 AI CHIP MARKET, BY TECHNOLOGY AND FUNCTION
4.6 AI CHIP MARKET, BY END USER
4.7 AI CHIP MARKET, BY REGION
4.8 AI CHIP MARKET, BY COUNTRY
5 MARKET OVERVIEW
5.1 INTRODUCTION
5.2 MARKET DYNAMICS
5.2.1 DRIVERS
5.2.1.1 Pressing need for large-scale data handling and real-time analytics
5.2.1.2 Rising adoption of autonomous vehicles
5.2.1.3 Surging use of GPUs and ASICs in AI servers
5.2.1.4 Continuous advancements in machine learning and deep learning technologies
5.2.1.5 Increasing penetration of AI servers
5.2.2 RESTRAINTS
5.2.2.1 Shortage of skilled workforce with technical know-how
5.2.2.2 Computational workloads and power consumption in AI Chip
5.2.2.3 Unreliability of AI algorithms
5.2.3 OPPORTUNITIES
5.2.3.1 Elevating demand for AI-based FPGA chips
5.2.3.2 Government initiatives to deploy AI-enabled defense systems
5.2.3.3 Rising trend of AI-driven diagnostics and treatments
5.2.3.4 Increasing investments in AI-enabled data centers by cloud service providers
5.2.3.5 Rise in adoption of AI-based ASIC technology
5.2.4 CHALLENGES
5.2.4.1 Data privacy concerns associated with AI platforms
5.2.4.2 Availability of limited structured data to develop efficient AI systems
5.2.4.3 Supply chain disruptions
5.3 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS
5.4 PRICING ANALYSIS
5.4.1 AVERAGE SELLING PRICE TREND OF KEY PLAYERS, BY COMPUTE
5.4.2 AVERAGE SELLING PRICE TREND, BY REGION
5.5 VALUE CHAIN ANALYSIS
5.6 ECOSYSTEM ANALYSIS
5.7 INVESTMENT AND FUNDING SCENARIO
5.8 TECHNOLOGY ANALYSIS
5.8.1 KEY TECHNOLOGIES
5.8.1.1 High-bandwidth Memory (HBM)
5.8.1.2 GenAI workload
5.8.2 COMPLEMENTARY TECHNOLOGIES
5.8.2.1 Data center power management and cooling system
5.8.2.2 High-speed interconnects
5.8.3 ADJACENT TECHNOLOGIES
5.8.3.1 AI development frameworks
5.8.3.2 Quantum AI
5.9 SERVER COST STRUCTURE/BILL OF MATERIAL
5.9.1 CPU SERVER
5.9.2 GPU SERVER
5.10 PENETRATION AND GROWTH OF AI SERVERS
5.11 UPCOMING DEPLOYMENT OF DATA CENTERS BY CLOUD SERVICE PROVIDERS (CSPS)
5.12 CLOUD SERVICE PROVIDERS' CAPEX
5.13 SERVER PROCUREMENT BY CLOUD SERVICE PROVIDERS, 2020-2029
5.14 PROCESSOR BENCHMARKING
5.14.1 GPU BENCHMARKING
5.14.2 CPU BENCHMARKING
5.15 PATENT ANALYSIS
5.16 TRADE ANALYSIS
5.16.1 IMPORT SCENARIO (HS CODE 854231)
5.16.2 EXPORT SCENARIO (HS CODE 854231)
5.17 KEY CONFERENCES AND EVENTS, 2024-2025
5.18 CASE STUDY ANALYSIS
5.18.1 CDW INTEGRATED AMD EPYC SOLUTIONS TO ENSURE ENERGY EFFICIENCY AND OPTIMUM SPACE UTILIZATION
5.18.2 OVH SAS LEVERAGED AMD EPYC PROCESSOR TO OPTIMIZE PERFORMANCE OF CLOUD SOLUTIONS IN AI WORKLOADS
5.18.3 INTEL XEON SCALABLE PROCESSORS POWER TENCENT CLOUD'S XIAOWEI INTELLIGENT SPEECH AND VIDEO SERVICE ACCESS PLATFORM
5.18.4 AIC HELPS WESTERN DIGITAL TO ENHANCE SSD TESTING AND VALIDATION EFFICIENCY USING AMD PROCESSOR
5.19 REGULATORY LANDSCAPE
5.19.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
5.19.2 STANDARDS
5.20 PORTER'S FIVE FORCES ANALYSIS
5.20.1 THREAT OF NEW ENTRANTS
5.20.2 THREAT OF SUBSTITUTES
5.20.3 BARGAINING POWER OF SUPPLIERS
5.20.4 BARGAINING POWER OF BUYERS
5.20.5 INTENSITY OF COMPETITION RIVALRY
5.21 KEY STAKEHOLDERS AND BUYING CRITERIA
5.21.1 KEY STAKEHOLDERS IN BUYING PROCESS
5.21.2 BUYING CRITERIA
6 AI CHIP MARKET, BY COMPUTE
6.1 INTRODUCTION
6.2 GPU
6.2.1 ABILITY TO HANDLE AI WORKLOADS AND PROCESS VAST DATA VOLUMES TO BOOST ADOPTION
6.3 CPU
6.3.1 RISING DEMAND FOR VERSATILE AND GENERAL-PURPOSE AI PROCESSING TO AUGMENT MARKET GROWTH
6.4 FPGA
6.4.1 GROWING NEED FOR FLEXIBILITY AND CUSTOMIZATION FOR AI WORKLOADS TO SPUR DEMAND
6.5 NPU
6.5.1 RISING DEMAND FOR HIGH-END SMARTPHONES TO DRIVE SEGMENTAL GROWTH
6.6 TPU
6.6.1 PRESSING NEED FOR FASTER PROCESSING IN AI RESEARCH AND APPLICATION DEVELOPMENT TO BOOST DEMAND
6.7 DOJO & FSD
6.7.1 ACCELERATING DEMAND FOR HIGH-PERFORMANCE, ENERGY-EFFICIENT AI PROCESSING IN AUTONOMOUS VEHICLES TO FUEL ADOPTION
6.8 TRAINIUM & INFERENTIA
6.8.1 ABILITY TO TRAIN COMPLEX AI AND DEEP LEARNING MODELS TO DRIVE ADOPTION
6.9 ATHENA ASIC
6.9.1 INCREASING NEED TO HANDLE COMPLEX NLP AND LANGUAGE-BASED AI TASKS TO ACCELERATE MARKET GROWTH
6.10 T-HEAD
6.10.1 RISING DEMAND FOR CUSTOMIZED, HIGH-PERFORMANCE AI CHIPS ACROSS CHINESE DATA CENTERS TO STIMULATE MARKET GROWTH
6.11 MTIA
6.11.1 META'S EXPANSION INTO AR, VR, AND METAVERSE TO FUEL MARKET GROWTH
6.12 LPU
6.12.1 INCREASING NEED TO HANDLE COMPLEX NLP AND LANGUAGE-BASED AI TASKS TO ACCELERATE MARKET GROWTH
6.13 OTHER ASIC
7 AI CHIP MARKET, BY MEMORY
7.1 INTRODUCTION
7.2 DDR
7.2.1 RISING ADOPTION OF AI-ENABLED CPUS IN DATA CENTERS TO SUPPORT MARKET GROWTH
7.3 HBM
7.3.1 ELEVATING NEED FOR HIGH THROUGHPUT IN DATA-INTENSIVE AI TASKS TO FUEL MARKET GROWTH
8 AI CHIP MARKET, BY NETWORK
8.1 INTRODUCTION
8.2 NIC/NETWORK ADAPTERS
8.2.1 INFINIBAND
8.2.1.1 Growing utilization of HPC and AI models to minimize latency and maximize throughput to boost segmental growth
8.2.2 ETHERNET
8.2.2.1 Rising demand for scalable and cost-effective networking solutions to propel growth
8.3 INTERCONNECTS
8.3.1 GROWING COMPLEXITY OF AI MODELS REQUIRING HIGH-BANDWIDTH DATA PATHS TO FUEL DEMAND
9 AI CHIP MARKET, BY TECHNOLOGY
9.1 INTRODUCTION
9.2 GENERATIVE AI
9.2.1 RULE-BASED MODELS
9.2.1.1 Rising need to detect fraud in finance sector to propel market
9.2.2 STATISTICAL MODELS
9.2.2.1 Requirement to make accurate predictions from complex data structures to boost segmental growth
9.2.3 DEEP LEARNING
9.2.3.1 Ability to advance AI technologies to boost demand
9.2.4 GENERATIVE ADVERSARIAL NETWORKS (GAN)
9.2.4.1 Pressing need to handle large-scale data to fuel segmental growth
9.2.5 AUTOENCODERS
9.2.5.1 Ability to compress and restructure data to ensure optimum storage space in data centers to stimulate demand
9.2.6 CONVOLUTIONAL NEURAL NETWORKS (CNNS)
9.2.6.1 Surging demand for realistic and high-quality images and videos to accelerate market growth
9.2.7 TRANSFORMER MODELS
9.2.7.1 Increasing utilization in image synthesis and captioning applications to foster segmental growth
9.3 MACHINE LEARNING
9.3.1 RISING USE IN IMAGE AND SPEECH RECOGNITION AND PREDICTIVE ANALYTICS TO CONTRIBUTE TO MARKET GROWTH
9.4 NATURAL LANGUAGE PROCESSING
9.4.1 INCREASING NEED FOR REAL-TIME APPLICATIONS TO SUPPORT MARKET GROWTH
9.5 COMPUTER VISION
9.5.1 ESCALATING NEED FOR ADVANCED PROCESSING CAPABILITIES TO BOOST DEMAND
10 AI CHIP MARKET, BY FUNCTION
10.1 INTRODUCTION
10.2 TRAINING
10.2.1 SURGING NEED TO PROCESS LARGE DATA SETS AND PERFORM PARALLEL COMPUTATION TO CREATE OPPORTUNITIES
10.3 INFERENCE
10.3.1 SURGING DEPLOYMENT ACROSS VARIOUS INDUSTRIES TO BOOST DEMAND
11 AI CHIP MARKET, BY END USER
11.1 INTRODUCTION
11.2 CONSUMER
11.2.1 GROWING ADOPTION OF AI-ENABLED PERSONAL DEVICES TO PROPEL MARKET
11.3 DATA CENTERS
11.3.1 CLOUD SERVICE PROVIDERS
11.3.1.1 Surging AI workloads and cloud adoption to stimulate market growth
11.3.2 ENTERPRISES
11.3.2.1 Escalating use of NLP, image recognition, and predictive analytics to create growth opportunities
11.3.2.2 Healthcare
11.3.2.2.1 Integration of AI in computer-aided drug discovery and development to foster market growth
11.3.2.3 BFSI
11.3.2.3.1 Surging need for fraud detection in financial institutions to boost demand
11.3.2.4 Automotive
11.3.2.4.1 Growing focus on safe and enhanced driving experiences to fuel demand
11.3.2.5 Retail & ecommerce
11.3.2.5.1 Increasing use of chatbots and virtual assistants to offer improved customer services to drive market
11.3.2.6 Media & entertainment
11.3.2.6.1 Real-time analysis of viewer preferences, engagement patterns, and demographic information to augment market growth
11.3.2.7 Others
11.4 GOVERNMENT ORGANIZATIONS
11.4.1 SIGNIFICANT FOCUS ON AUTOMATING ROUTINE TASKS AND EXTRACTING REAL-TIME ACTIONABLE INSIGHTS TO SUPPORT MARKET GROWTH
12 AI CHIP MARKET, BY REGION
12.1 INTRODUCTION
12.2 NORTH AMERICA
12.2.1 MACROECONOMIC OUTLOOK FOR NORTH AMERICA
12.2.2 US
12.2.2.1 Government-led initiatives to boost semiconductor manufacturing to drive market
12.2.3 CANADA
12.2.3.1 Growing emphasis on commercializing AI to spur demand
12.2.4 MEXICO
12.2.4.1 Increasing shift toward digital platforms and cloud-based solutions to accelerate demand
12.3 EUROPE
12.3.1 MACROECONOMIC OUTLOOK FOR EUROPE
12.3.2 UK
12.3.2.1 Growing investments in data center infrastructure to boost demand
12.3.3 GERMANY
12.3.3.1 Presence of robust industrial base to offer lucrative growth opportunities
12.3.4 FRANCE
12.3.4.1 Increasing number of AI startups to accelerate demand
12.3.5 ITALY
12.3.5.1 Rising adoption of digitalization in automotive and healthcare sectors to drive market
12.3.6 SPAIN
12.3.6.1 Growing collaborations and partnerships among AI manufacturers to spur demand
12.3.7 REST OF EUROPE
12.4 ASIA PACIFIC
12.4.1 MACROECONOMIC OUTLOOK FOR ASIA PACIFIC
12.4.2 CHINA
12.4.2.1 Surge in research funding and implementation of supportive regulatory policy to augment market growth
12.4.3 JAPAN
12.4.3.1 Rising adoption of AI chips to advance robotic systems to offer lucrative growth opportunities
12.4.4 INDIA
12.4.4.1 Government-led initiatives to boost AI infrastructure to foster market growth
12.4.5 SOUTH KOREA
12.4.5.1 Thriving semiconductor industry to drive market growth
12.4.6 REST OF ASIA PACIFIC
12.5 ROW
12.5.1 MACROECONOMIC OUTLOOK FOR ROW
12.5.2 MIDDLE EAST
12.5.2.1 Growing emphasis on digital transformation and technological innovation to drive market growth
12.5.2.2 GCC countries
12.5.2.3 Rest of Middle East
12.5.3 AFRICA
12.5.3.1 Rising internet penetration and mobile subscriptions to offer lucrative growth opportunities
12.5.4 SOUTH AMERICA
12.5.4.1 Growing need to store vast volumes of data to boost demand
13 COMPETITIVE LANDSCAPE
13.1 INTRODUCTION
13.2 KEY PLAYER STRATEGIES/RIGHT TO WIN, 2019-2024
13.3 REVENUE ANALYSIS, 2021-2023
13.4 MARKET SHARE ANALYSIS, 2023
13.5 COMPANY VALUATION AND FINANCIAL METRICS
13.6 BRAND/PRODUCT COMPARISON
13.7 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2023
13.7.1 STARS
13.7.2 EMERGING LEADERS
13.7.3 PERVASIVE PLAYERS
13.7.4 PARTICIPANTS
13.7.5 COMPANY FOOTPRINT: KEY PLAYERS, 2023
13.7.5.1 Company footprint
13.7.5.2 Compute footprint
13.7.5.3 Memory footprint
13.7.5.4 Network footprint
13.7.5.5 Technology footprint
13.7.5.6 Function footprint
13.7.5.7 End user footprint
13.7.5.8 Region footprint
13.8 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2023
