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The Global Market for AI Chips 2024-2034

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    Report

  • 252 Pages
  • November 2023
  • Region: Global
  • Future Markets, Inc
  • ID: 5902665
Description Jump to:

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 microprocessor 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, and programming complexities
  • Geopolitical implications and policy considerations
  • Environmental stewardship priorities and frameworks


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Table of Contents


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 wearables
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 134 (133 company profiles)9 REFERENCES
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 the 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 chip 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.

Companies Mentioned (Partial List)

A selection of companies mentioned in this report includes, but is not limited to:

  • 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
  • Tenstorrent

Methodology

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About the Artificial Intelligence Chipset Market

The Artificial Intelligence Chipset market is a rapidly growing sector of the Artificial Intelligence industry. AI Chipsets are specialized computer chips designed to enable AI-based applications and services. They are used in a variety of applications, such as autonomous vehicles, robotics, and machine learning. AI Chipsets are designed to provide high performance and low power consumption, making them ideal for use in embedded systems. AI Chipsets are also used in edge computing, where they are used to process data locally, rather than relying on cloud computing. AI Chipsets are typically designed to support a variety of AI algorithms, such as deep learning, natural language processing, and computer vision. They are also designed to be highly scalable, allowing them to be used in a variety of applications. Some of the major companies in the Artificial Intelligence Chipset market include Intel, NVIDIA, Qualcomm, and AMD. Other companies in the market include Google, Apple, and IBM. Show Less Read more

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