The Global Market for AI Chips 2024-2034
The speed of development of generative AI, boosted by the success of OpenAI's ChatGPT, is raising investor interest in companies working on AI-related infrastructure such as AI chips. Artificial Intelligence (AI) chips are a new generation of microprocessors chips designed to efficiently run AI-related workloads like machine learning, neural networks, and deep learning. As AI technology has advanced rapidly in recent years, there has been increasing demand for hardware optimized for AI processing versus general-purpose computer chips. AI chips are designed to run such AI algorithms faster and more efficiently than traditional processors. This has driven extensive research, development, and investment into AI chip technology by established and emerging companies.
The Global Market for AI Chips 2024-2034 provides a comprehensive analysis of the global AI chip landscape. Spanning over 300 pages, the report covers AI chip technology fundamentals, key capabilities enabled, applications across industries, market segmentation, regional trends, major players, start-up ecosystem, funding and investments, challenges, manufacturing and supply chain dynamics, architectural innovations, sustainability impacts, and the future outlook for these transformative technologies.
Multiple data tables and charts quantify market size projections to 2034 by region, vertical, chip type, and more. Profiles of over 100 companies highlight competitive positioning. Expert insights identify growth opportunities as specialized AI hardware progresses. The Global Market for AI Chips 2024-2034 is ideal for semiconductor industry participants, tech investors, and companies strategizing AI chip adoption to inform planning amid this rapidly evolving space.
Report contents include:
AI Chip Technology Fundamentals
Architectures like GPUs, ASICs, neuromorphic chips
Processing capabilities enabled by AI hardware
Development history and ecosystem
Market Landscape and Segmentation
Market size forecasts globally and by region
Breakdown by chip type - ASICs, GPUs, CPUs, FPGAs
Split by training vs inference workloads
Segmentation by end-use industry vertical
Regional Analysis
AI chip development trends in China
Government policies in the US, Europe, South Korea, Japan
Edge AI advances by country
Industry Drivers and Adoption Factors
Key market growth drivers
Government funding and R&D initiatives
Corporate investments fuelling innovation
Applications propelling demand across domains
Competitive Environment
Profiles of over 130 leading companies. Companies profiled include AMD, Astrus, Celestial AI,
Cerebras, d-Matrix, DEEPX, EdgeCortix® Inc., Etched.ai, Enfabrica, Enflame, Google, Horizon Robotics, IBM, Kneron, Lightmatter, Modular, MediaTek Inc, Mythic, Neuchips, Nvidia, Panmnesia, Rebellions, Samsung, SambaNova Systems, Sapeon, SiMa.ai, SpiNNcloud Systems GmbH and Tenstorrent.
Startups advancing new architectures
Silicon giants leveraging semiconductor expertise
Cloud providers and automotive supplier activity
Technology Innovations
Novel materials, packaging, software abstractions
Architectural advances in processing, memory, interconnects
Progress in manufacturing techniques like lithography, 3D stacking
Challenges and Sustainability
Design, benchmarking, programming complexities
Geopolitical implications and policy considerations
Environmental stewardship priorities and frameworks
- RESEARCH METHODOLOGY
- INTRODUCTION
- What is an AI chip?
- AI Acceleration
- Hardware & Software Co-Design
- Key capabilities
- History of AI Chip Development
- Applications
- Table Markets and applications for AI chips.
- AI Chip Architectures
- Table AI Chip Architectures.
- Computing requirements
- Table Computing requirements and constraints.
- Table Computing requirements and constraints by applications.
- Semiconductor packaging
- Evolution from 1D to 3D semiconductor packaging
- AI chip market landscape
- China
- USA
- Europe
- Rest of Asia
- Edge AI
- Table Advantages and disadvantages of edge AI.
- Edge vs Cloud
- Table Edge vs Cloud.
- Edge devices that utilize AI chips
- Table Edge devices that utilize AI chips.
- Players in edge AI chips
- Table Players in edge AI chips.
- Inference at the edge
- Market drivers
- Table Market drivers for AI Chips.
- Government funding and initiatives
- Table AI chip government funding and initiatives.
- Funding and investments
- Table AI chips funding and investment, by company.
- Market challenges
- Table Market challenges in AI chips.
- Market players
- Table Key players in AI chips.
- Future Outlook for AI Chips
- Specialization
- 3D System Integration
- Software Abstraction Layers
- Edge-Cloud Convergence
- Environmental Sustainability
- Neuromorphic Photonics
- New Materials
- Efficiency Improvements
- Automated Chip Generation
- AI roadmap
- What is an AI chip?
- AI CHIP FABRICATION
- Supply chain
- Table AI Chip Supply Chain.
- Fab investments and capabilities
- Table Fab investments and capabilities.
- Table Comparison of AI chip fabrication capabilities between IDMs (integrated device manufacturers) and dedicated foundries.
- Manufacturing advances
- Chiplets
- 3D Fabrication
- Algorithm-Hardware Co-Design
- Advanced Lithography
- Novel Devices
- Supply chain
- AI CHIP ARCHITECTURES
- Distributed Parallel Processing
- Optimized Data Flow
- Flexible vs. Specialized Designs
- Hardware for Training vs. Inference
- Software Programmability
- Architectural Optimization Goals
- Table Goals driving the exploration into AI chip architectures.
- Innovations
- Specialized Processing Units
- Dataflow Optimization
- Model Compression
- Biologically-Inspired Designs
- Table Concepts from neuroscience influence architecture.
- Analog Computing
- Photonic Connectivity
- Sustainability
- Energy Efficiency
- Green Data Centers
- Eco-Electronics
- Reusable Architectures & IP
- Regulated Lifecycles
- AI for Sustainability
- AI Model Efficiency
- Companies, by architecture
- Table Companies by Architecture.
- TYPES OF AI CHIPS
- Training Accelerators
- Table Types of training accelerators for AI chips.
- Inference Accelerators
- Table Types of inference accelerators for AI chips.
- Automotive AI Chips
- Table Types of Automotive AI chips.
- Smart Device AI Chips
- Table Smart device AI chips.
- Cloud Data Center Chips
- Table Types of cloud data center AI chips.
- Edge AI Chips
- Table Key types of edge AI chips.
- Neuromorphic Chips
- Table Types of neuromorphic chips and their attributes.
- FPGA-Based Solutions
- Table Types of FPGA-based solutions for AI acceleration.
- Multi-Chip Modules
- Table Types of multi-chip module (MCM) integration approaches for AI chips.
- Emerging technologies
- Novel Materials
- Table 2D materials in AI hardware.
- Table Photonic materials for AI hardware.
- Table Spintronic materials for AI hardware.
- Table Phase change materials for AI hardware.
- Table Neuromorphic materials in AI hardware.
- Advanced Packaging
- Table Techniques for combining chiplets and dies using advanced packaging for AI chips.
- Software Abstraction
- Environmental Sustainability
- Novel Materials
- Specialized components
- Sensor Interfacing
- Table Types of sensors.
- Memory Technologies
- Software Frameworks
- Data Center Design
- Sensor Interfacing
- Training Accelerators
- AI CHIP MARKETS
- Market map
- Data Centers
- Market overview
- Market players
- Hardware
- Trends
- Automotive
- Market overview
- Market outlook
- Autonomous Driving
- Table Key AI chip products and solutions targeting automotive applications.
- Increasing power demands
- Market players
- Industry 4.0
- Market overview
- Applications
- Market players
- Smartphones
- Market overview
- Table AI versus non-AI smartphones
- Commercial examples
- Smartphone chipset market
- Process nodes
- Table Key chip fabrication process nodes used by various mobile AI chip designers.
- Market overview
- Tablets
- Market overview
- Market players
- Table AI versus non AI tablets.
- IoT & IIoT
- Market overview
- AI on the IoT edge
- Consumer smart appliances
- Market players
- Computing
- Market overview
- Personal computers
- Parallel computing
- Low-precision computing
- Market players
- Table Market players in AI chips for personal, parallel, and low-precision computing.
- Drones & Robotics
- Market overview
- Market players
- Table AI chip company products for drones and robotics.
- Wearables, AR glasses and hearables
- Market overview
- Applications
- Table Applications of AI chips in wearable devices.
- Market players
- Sensors
- Market overview
- Challenges
- Applications
- Table Applications of ai chips and sensors and structural health monitoring.
- Market players
- Life Sciences
- Market overview
- Applications
- Table Applications of AI chips in life sciences.
- Market players
- GLOBAL MARKET REVENUES AND COSTS
- Costs
- Table AI chip costs analysis-design, operation and fabrication.
- Table Design, manufacturing, testing, and operational costs associated with leading-edge process nodes for AI chips.
- Table Assembly, test, and packaging (ATP) costs associated with manufacturing AI chips.
- Revenues by chip type, 2020-2034
- Table Global market revenues by chip type, 2020-2034 (billions USD).
- Revenues by market, 2020-2034
- Table Global market revenues by market, 2020-2034 (billions USD).
- Revenues by region, 2020-2034
- Table Global market revenues by region, 2020-2034 (billions USD).
- Costs
- COMPANY PROFILES
- AiM Future
- Aistorm
- Advanced Micro Devices (AMD)
- Table AMD AI chip range.
- Alpha ICs
- Amazon Web Services (AWS)
- Ambarella, Inc.
- Table Applications of CV3-AD685 in autonomous driving. Image credit: Ambarella.
- Anaflash
- Andes Technology
- Apple
- Table Evolution of Apple Neural Engine.
- Arm
- Astrus, Inc.
- Axelera AI
- Axera Semiconductor
- Baidu, Inc.
- BirenTech
- Black Sesame Technologies
- Blaize
- Blumind Inc.
- Brainchip Holdings Ltd.
- Cambricon
- Ccvui (Xinsheng Intelligence)
- Celestial AI
- Cerebras Systems
- Ceremorphic
- ChipIntelli
- CIX Technology
- Corerain Technologies
- DeGirum
- Denglin Technology
- DEEPX
- d-Matrix
- Eeasy Technology
- EdgeCortix
- Efinix
- Enerzai
- Enfabrica
- Enflame
- Esperanto Technologies
- Etched.ai
- Evomotion
- Expedera
- Flex Logix
- FuriosaAI
- GrAI Matter Labs
- Graphcore
- GreenWaves Technologies
- Groq
- Gwanak Analog Co., Ltd.
- Hailo
- Horizon Robotics
- Houmo.ai
- Huawei
- IBM
- Iluvatar CoreX
- Innatera Nanosystems
- Intel
- Intellifusion
- Intelligent Hardware Korea (IHWK)
- Inuitive
- Jeejio
- Kalray SA
- Kinara
- KIST (Korea Institute of Science and Technology)
- Kneron
- Kunlunxin Technology
- Lightmatter
- Lightstandard Technology
- Lightelligence
- Lumai
- Luminous Computing
- MatX
- MediaTek
- Meta
- Microsoft
- Mobilint
- Modular
- Moffett AI
- Moore Threads
- Mythic
- Nanjing SemiDrive Technology
- Nano-Core Chip
- National Chip
- Neuchips
- NeuronBasic
- NeuReality
- NeuroBlade
- NextVPU
- Nextchip Co., Ltd.
- NXP Semiconductors
- Nvidia
- Oculi
- Panmnesia
- Perceive
- Pingxin Technology
- Quadric
- Qualcomm
- Rain
- Rebellions, Inc.
- Recogni
- RiVAI
- Roviero
- Salience Labs
- SambaNova Systems
- Samsung
- Sapeon
- Seehi
- Shencong Semiconductor (ShensiliCon)
- Shenzhen Qiyang
- SiMa.ai
- Solitorch
- SpiNNcloud Systems
- SynSense Technology
- Tachyum
- T-Head (Pingtouge Semiconductor)
- Tecorigin
- Tencent Holdings
- Tenstorrent
- Tesla
- Tsing Micro
- TSMC
- Untether AI
- Upmem
- Shenzhen Youzhichuangxin Technologies Co., Ltd.,
- UXFACTORY
- Vast AI Tech
- Videantis
- Vimicro Corporation
- Weeteq
- Witmem Technology
- Yizhu Technology
- ZenTech
- Zhonghao Xinying Technology
- REFERENCES
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