DUBLIN: The "The Global Market for AI Chips 2024-2034" report has been added to ResearchAndMarkets.com's offering.

The Global Market for AI Chips 2024-2034 provides a comprehensive analysis of the global AI chip landscape.

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.

Notably, edge AI advances vary by country, showcasing diverse approaches to this emerging field. Regarding industry drivers and adoption factors, key market growth drivers, government funding, and corporate investments fuel innovation, with applications across various domains propelling demand. In technology innovations, advancements in novel materials, packaging, and software abstractions, along with architectural progress in processing and memory, drive AI evolution.

Manufacturing techniques like lithography and 3D stacking further contribute to technological innovation. However, challenges remain, including design complexities, geopolitical implications, and environmental stewardship priorities, underscoring the need for sustainable AI development frameworks.

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.

In regional analysis, China's AI chip development trends stand out, reflecting its commitment to technological advancement. Meanwhile, government policies in the US, Europe, South Korea, and Japan play significant roles in shaping AI innovation landscapes.

Competitive Environment

• Startups advancing new architectures
• Silicon giants leveraging semiconductor expertise
• Cloud providers and automotive supplier activity

Profiles of over 130 leading companies including

• 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

Key Topics Covered:

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 (133 company profiles)

9 REFERENCES

For more information about this report visit https://www.researchandmarkets.com/r/6eytim

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Fonte: Business Wire