The Global Market for AI Chips 2024-2034

Description

List of Tables

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

Product Code: ND23

1. RESEARCH METHODOLOGY

2. INTRODUCTION

2.1. What is an AI chip?
- 2.1.1. AI Acceleration
- 2.1.2. Hardware & Software Co-Design
2.2. Key capabilities
2.3. History of AI Chip Development
2.4. Applications
2.5. AI Chip Architectures
2.6. Computing requirements
2.7. Semiconductor packaging
- 2.7.1. Evolution from 1D to 3D semiconductor packaging
2.8. AI chip market landscape
- 2.8.1. China
- 2.8.2. USA
  - 2.8.2.1. The US CHIPS and Science Act of 2022
- 2.8.3. Europe
  - 2.8.3.1. The European Chips Act of 2022
- 2.8.4. Rest of Asia
  - 2.8.4.1. South Korea
  - 2.8.4.2. Japan
  - 2.8.4.3. Taiwan
2.9. Edge AI
- 2.9.1. Edge vs Cloud
- 2.9.2. Edge devices that utilize AI chips
- 2.9.3. Players in edge AI chips
- 2.9.4. Inference at the edge
2.10. Market drivers
2.11. Government funding and initiatives
2.12. Funding and investments
2.13. Market challenges
2.14. Market players
2.15. Future Outlook for AI Chips
- 2.15.1. Specialization
- 2.15.2. 3D System Integration
- 2.15.3. Software Abstraction Layers
- 2.15.4. Edge-Cloud Convergence
- 2.15.5. Environmental Sustainability
- 2.15.6. Neuromorphic Photonics
- 2.15.7. New Materials
- 2.15.8. Efficiency Improvements
- 2.15.9. Automated Chip Generation
2.16. AI roadmap

3. AI CHIP FABRICATION

3.1. Supply chain
3.2. Fab investments and capabilities
3.3. Manufacturing advances
- 3.3.1. Chiplets
- 3.3.2. 3D Fabrication
- 3.3.3. Algorithm-Hardware Co-Design
- 3.3.4. Advanced Lithography
- 3.3.5. Novel Devices

4. AI CHIP ARCHITECTURES

4.1. Distributed Parallel Processing
4.2. Optimized Data Flow
4.3. Flexible vs. Specialized Designs
4.4. Hardware for Training vs. Inference
4.5. Software Programmability
4.6. Architectural Optimization Goals
4.7. Innovations
- 4.7.1. Specialized Processing Units
- 4.7.2. Dataflow Optimization
- 4.7.3. Model Compression
- 4.7.4. Biologically-Inspired Designs
- 4.7.5. Analog Computing
- 4.7.6. Photonic Connectivity
4.8. Sustainability
- 4.8.1. Energy Efficiency
- 4.8.2. Green Data Centers
- 4.8.3. Eco-Electronics
- 4.8.4. Reusable Architectures & IP
- 4.8.5. Regulated Lifecycles
- 4.8.6. AI for Sustainability
- 4.8.7. AI Model Efficiency
4.9. Companies, by architecture

5. TYPES OF AI CHIPS

5.1. Training Accelerators
5.2. Inference Accelerators
5.3. Automotive AI Chips
5.4. Smart Device AI Chips
5.5. Cloud Data Center Chips
5.6. Edge AI Chips
5.7. Neuromorphic Chips
5.8. FPGA-Based Solutions
5.9. Multi-Chip Modules
5.10. Emerging technologies
- 5.10.1. Novel Materials
  - 5.10.1.1. 2D materials
  - 5.10.1.2. Photonic materials
  - 5.10.1.3. Spintronic materials
  - 5.10.1.4. Phase change materials
  - 5.10.1.5. Neuromorphic materials
- 5.10.2. Advanced Packaging
- 5.10.3. Software Abstraction
- 5.10.4. Environmental Sustainability
5.11. Specialized components
- 5.11.1. Sensor Interfacing
- 5.11.2. Memory Technologies
  - 5.11.2.1. HBM stacks
  - 5.11.2.2. GDDR
  - 5.11.2.3. SRAM
  - 5.11.2.4. STT-RAM
  - 5.11.2.5. ReRAM
- 5.11.3. Software Frameworks
- 5.11.4. Data Center Design

6. AI CHIP MARKETS

6.1. Market map
6.2. Data Centers
- 6.2.1. Market overview
- 6.2.2. Market players
- 6.2.3. Hardware
- 6.2.4. Trends
6.3. Automotive
- 6.3.1. Market overview
- 6.3.2. Market outlook
- 6.3.3. Autonomous Driving
  - 6.3.3.1. Market players
- 6.3.4. Increasing power demands
- 6.3.5. Market players
6.4. Industry 4.0
- 6.4.1. Market overview
- 6.4.2. Applications
- 6.4.3. Market players
6.5. Smartphones
- 6.5.1. Market overview
- 6.5.2. Commercial examples
- 6.5.3. Smartphone chipset market
- 6.5.4. Process nodes
6.6. Tablets
- 6.6.1. Market overview
- 6.6.2. Market players
6.7. IoT & IIoT
- 6.7.1. Market overview
- 6.7.2. AI on the IoT edge
- 6.7.3. Consumer smart appliances
- 6.7.4. Market players
6.8. Computing
- 6.8.1. Market overview
- 6.8.2. Personal computers
- 6.8.3. Parallel computing
- 6.8.4. Low-precision computing
- 6.8.5. Market players
6.9. Drones & Robotics
- 6.9.1. Market overview
- 6.9.2. Market players
6.10. Wearables, AR glasses and hearables
- 6.10.1. Market overview
- 6.10.2. Applications
- 6.10.3. Market players
6.11. Sensors
- 6.11.1. Market overview
- 6.11.2. Challenges
- 6.11.3. Applications
- 6.11.4. Market players
6.12. Life Sciences
- 6.12.1. Market overview
- 6.12.2. Applications
- 6.12.3. Market players

7. GLOBAL MARKET REVENUES AND COSTS

7.1. Costs
7.2. Revenues by chip type, 2020-2034
7.3. Revenues by market, 2020-2034
7.4. Revenues by region, 2020-2034

8. COMPANY PROFILES (133 company profiles)

9. REFERENCES

Product Code: ND23

List of Tables

Table 1. Markets and applications for AI chips
Table 2. AI Chip Architectures
Table 3. Computing requirements and constraints
Table 4. Computing requirements and constraints by applications
Table 5. Advantages and disadvantages of edge AI
Table 6. Edge vs Cloud
Table 7. Edge devices that utilize AI chips
Table 8. Players in edge AI chips
Table 9. Market drivers for AI Chips
Table 10. AI chip government funding and initiatives
Table 11. AI chips funding and investment, by company
Table 12. Market challenges in AI chips
Table 13. Key players in AI chips
Table 14. AI Chip Supply Chain
Table 15. Fab investments and capabilities
Table 16. Comparison of AI chip fabrication capabilities between IDMs (integrated device manufacturers) and dedicated foundries
Table 17. Goals driving the exploration into AI chip architectures
Table 18. Concepts from neuroscience influence architecture
Table 19. Companies by Architecture
Table 20. Types of training accelerators for AI chips
Table 21. Types of inference accelerators for AI chips
Table 22. Types of Automotive AI chips
Table 23. Smart device AI chips
Table 24. Types of cloud data center AI chips
Table 25. Key types of edge AI chips
Table 26. Types of neuromorphic chips and their attributes
Table 27. Types of FPGA-based solutions for AI acceleration
Table 28. Types of multi-chip module (MCM) integration approaches for AI chips
Table 29. 2D materials in AI hardware
Table 30. Photonic materials for AI hardware
Table 31. Spintronic materials for AI hardware
Table 32. Phase change materials for AI hardware
Table 33. Neuromorphic materials in AI hardware
Table 34. Techniques for combining chiplets and dies using advanced packaging for AI chips
Table 35. Types of sensors
Table 36. Key AI chip products and solutions targeting automotive applications
Table 37. AI versus non-AI smartphones
Table 38. Key chip fabrication process nodes used by various mobile AI chip designers
Table 39. AI versus non AI tablets
Table 40. Market players in AI chips for personal, parallel, and low-precision computing
Table 41. AI chip company products for drones and robotics
Table 42. Applications of AI chips in wearable devices
Table 43. Applications of ai chips and sensors and structural health monitoring
Table 44. Applications of AI chips in life sciences
Table 45. AI chip costs analysis-design, operation and fabrication
Table 46. Design, manufacturing, testing, and operational costs associated with leading-edge process nodes for AI chips
Table 47. Assembly, test, and packaging (ATP) costs associated with manufacturing AI chips
Table 48. Global market revenues by chip type, 2020-2034 (billions USD)
Table 49. Global market revenues by market, 2020-2034 (billions USD)
Table 50. Global market revenues by region, 2020-2034 (billions USD)
Table 51. AMD AI chip range
Table 52. Applications of CV3-AD685 in autonomous driving
Table 53. Evolution of Apple Neural Engine

List of Figures

Figure 1. Nvidia H200 AI Chip
Figure 2. History of AI development
Figure 3. AI roadmap
Figure 4. Nvidia A100 GPU
Figure 5. Google Cloud TPUs
Figure 6. Groq Node
Figure 7. Intel Movidius Myriad X
Figure 8. Qualcomm Cloud AI 100
Figure 9. Tesla FSD Chip
Figure 10. Qualcomm Snapdragon
Figure 11. AI chio market map
Figure 12. Global market revenues by chip type, 2020-2034 (billions USD)
Figure 13. Global market revenues by market 2020-2034 (billions USD)
Figure 14. Global market revenues by region, 2020-2034 (billions USD)
Figure 15. AMD Radeon Instinct
Figure 16. AMD Ryzen 7040
Figure 17. Alveo V70
Figure 18. Versal Adaptive SOC
Figure 19. AMD's MI300 chip
Figure 20. Cerebas WSE-2
Figure 21. DeepX NPU DX-GEN1
Figure 22. InferX X1
Figure 23. "Warboy"(AI Inference Chip)
Figure 24. Google TPU
Figure 25. GrAI VIP
Figure 26. Colossus™ MK2 GC200 IPU
Figure 27. GreenWave's GAP8 and GAP9 processors
Figure 28. Journey 5
Figure 29. IBM Telum processor
Figure 30. 11th Gen Intel® Core™ S-Series
Figure 31. Envise
Figure 32. Pentonic 2000
Figure 33. Meta Training and Inference Accelerator (MTIA)
Figure 34. Azure Maia 100 and Cobalt 100 chips
Figure 35. Mythic MP10304 Quad-AMP PCIe Card
Figure 36. Nvidia H200 AI chip
Figure 37. Grace Hopper Superchip
Figure 38. Panmnesia memory expander module (top) and chassis loaded with switch and expander modules (below)
Figure 39. Cloud AI 100
Figure 40. Peta Op chip
Figure 41. Cardinal SN10 RDU
Figure 42. MLSoC™
Figure 43. Grayskull
Figure 44. Tesla D1 chip

The Global Market for AI Chips 2024-2034

Description

Table of Contents

List of Tables

Report contents include:

TABLE OF CONTENTS

1. RESEARCH METHODOLOGY

2. INTRODUCTION

3. AI CHIP FABRICATION

4. AI CHIP ARCHITECTURES

5. TYPES OF AI CHIPS

6. AI CHIP MARKETS

7. GLOBAL MARKET REVENUES AND COSTS

8. COMPANY PROFILES (133 company profiles)

9. REFERENCES

List of Tables

List of Figures