DUBLIN, Nov. 20, 2023 /PRNewswire/ — The “Global Market for AI Chips 2024-2034” report has been added to  ResearchAndMarkets.com’s offering.

Artificial intelligence (AI) chips are specialized microchips 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.

Competitive Environment

• Profiles of over 100 leading companies. Companies profiled include AMD, Cerebras, d-Matrix, Etched.ai, Enfabrica, Enflame, Google, Horizon Robotics, IBM, Kneron, Lightmatter, MatX, Modular, MediaTek Inc, Mythic, Neuchips, Nvidia, Qyber, Rain Neuromorphics, Rebellions, Samsung, Samba Nova, Tenstorrent, and Tiny.
• Startups advancing new architectures
• Silicon giants leveraging semiconductor expertise
• Cloud providers and automotive supplier activity

Key Topics Covered:

1 RESEARCH METHODOLOGY

2 INTRODUCTION
2.1 What is an AI chip?
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
2.8 AI chip market landscape
2.9 Edge AI
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
2.16 AI roadmap

3 AI CHIP FABRICATION
3.1 Supply chain
3.2 Fab investments and capabilities
3.3 Manufacturing advances

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
4.8 Sustainability
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
5.11 Specialized components

6 AI CHIP MARKETS
6.1 Market map
6.2 Data Centers
6.3 Automotive
6.4 Industry 4.0
6.5 Smartphones
6.6 Tablets
6.7 IoT & IIoT
6.8 Computing
6.8.1 Personal computers
6.8.2 Parallel computing
6.8.3 Low-precision computing
6.9 Drones & Robotics
6.10 Wearables, AR glasses and hearables
6.11 Sensors

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 (114 company profiles)

For more information about this report visit https://www.researchandmarkets.com/r/d937re

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