The global Machine Learning in Chip Design market was valued at US$ million in 2023 and is anticipated to reach US$ million by 2030, witnessing a CAGR of % during the forecast period 2024-2030.
North American market for Machine Learning in Chip Design is estimated to increase from $ million in 2023 to reach $ million by 2030, at a CAGR of % during the forecast period of 2024 through 2030.
Asia-Pacific market for Machine Learning in Chip Design is estimated to increase from $ million in 2023 to reach $ million by 2030, at a CAGR of % during the forecast period of 2024 through 2030.
The global market for Machine Learning in Chip Design in IDM is estimated to increase from $ million in 2023 to $ million by 2030, at a CAGR of % during the forecast period of 2024 through 2030.
The major global companies of Machine Learning in Chip Design include IBM, Applied Materials, Siemens, Google(Alphabet), Cadence Design Systems, Synopsys, Intel, NVIDIA and Mentor Graphics, etc. In 2023, the world's top three vendors accounted for approximately % of the revenue.
This report aims to provide a comprehensive presentation of the global market for Machine Learning in Chip Design, with both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding Machine Learning in Chip Design.
Report Scope
The Machine Learning in Chip Design market size, estimations, and forecasts are provided in terms of revenue ($ millions), considering 2023 as the base year, with history and forecast data for the period from 2019 to 2030. This report segments the global Machine Learning in Chip Design market comprehensively. Regional market sizes, concerning products by Type, by Application, and by players, are also provided.
For a more in-depth understanding of the market, the report provides profiles of the competitive landscape, key competitors, and their respective market ranks. The report also discusses technological trends and new product developments.
The report will help the Machine Learning in Chip Design companies, new entrants, and industry chain related companies in this market with information on the revenues, sales volume, and average price for the overall market and the sub-segments across the different segments, by company, by Type, by Application, and by regions.
麻豆原创 Segmentation
By Company
IBM
Applied Materials
Siemens
Google(Alphabet)
Cadence Design Systems
Synopsys
Intel
NVIDIA
Mentor Graphics
Flex Logix Technologies
Arm Limited
Kneron
Graphcore
Hailo
Groq
Mythic AI
Segment by Type
Supervised Learning
Semi-supervised Learning
Unsupervised Learning
Reinforcement Learning
Segment by Application
IDM
Foundry
By Region
North America
United States
Canada
Europe
Germany
France
UK
Italy
Russia
Nordic Countries
Rest of Europe
Asia-Pacific
China
Japan
South Korea
Southeast Asia
India
Australia
Rest of Asia
Latin America
Mexico
Brazil
Rest of Latin America
Middle East & Africa
Turkey
Saudi Arabia
UAE
Rest of MEA
Chapter Outline
Chapter 1: Introduces the report scope of the report, executive summary of different market segments (by Type, by Application, etc), including the market size of each market segment, future development potential, and so on. It offers a high-level view of the current state of the market and its likely evolution in the short to mid-term, and long term.
Chapter 2: Introduces executive summary of global market size, regional market size, this section also introduces the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by companies in the industry, and the analysis of relevant policies in the industry.
Chapter 3: Detailed analysis of Machine Learning in Chip Design companies鈥� competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.
Chapter 4: Provides the analysis of various market segments by Type, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments.
Chapter 5: Provides the analysis of various market segments by Application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.
Chapter 6, 7, 8, 9, 10: North America, Europe, Asia Pacific, Latin America, Middle East and Africa segment by country. It provides a quantitative analysis of the market size and development potential of each region and its main countries and introduces the market development, future development prospects, market space, and capacity of each country in the world.
Chapter 11: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product sales, revenue, price, gross margin, product introduction, recent development, etc.
Chapter 12: The main points and conclusions of the report.
Please Note - This is an on demand report and will be delivered in 2 business days (48 hours) post payment.
1 Report Overview
1.1 Study Scope
1.2 麻豆原创 Analysis by Type
1.2.1 Global Machine Learning in Chip Design 麻豆原创 Size Growth Rate by Type: 2019 VS 2023 VS 2030
1.2.2 Supervised Learning
1.2.3 Semi-supervised Learning
1.2.4 Unsupervised Learning
1.2.5 Reinforcement Learning
1.3 麻豆原创 by Application
1.3.1 Global Machine Learning in Chip Design 麻豆原创 Growth by Application: 2019 VS 2023 VS 2030
1.3.2 IDM
1.3.3 Foundry
1.4 Study Objectives
1.5 Years Considered
1.6 Years Considered
2 Global Growth Trends
2.1 Global Machine Learning in Chip Design 麻豆原创 Perspective (2019-2030)
2.2 Machine Learning in Chip Design Growth Trends by Region
2.2.1 Global Machine Learning in Chip Design 麻豆原创 Size by Region: 2019 VS 2023 VS 2030
2.2.2 Machine Learning in Chip Design Historic 麻豆原创 Size by Region (2019-2024)
2.2.3 Machine Learning in Chip Design Forecasted 麻豆原创 Size by Region (2025-2030)
2.3 Machine Learning in Chip Design 麻豆原创 Dynamics
2.3.1 Machine Learning in Chip Design Industry Trends
2.3.2 Machine Learning in Chip Design 麻豆原创 Drivers
2.3.3 Machine Learning in Chip Design 麻豆原创 Challenges
2.3.4 Machine Learning in Chip Design 麻豆原创 Restraints
3 Competition Landscape by Key Players
3.1 Global Top Machine Learning in Chip Design Players by Revenue
3.1.1 Global Top Machine Learning in Chip Design Players by Revenue (2019-2024)
3.1.2 Global Machine Learning in Chip Design Revenue 麻豆原创 Share by Players (2019-2024)
3.2 Global Machine Learning in Chip Design 麻豆原创 Share by Company Type (Tier 1, Tier 2, and Tier 3)
3.3 Players Covered: Ranking by Machine Learning in Chip Design Revenue
3.4 Global Machine Learning in Chip Design 麻豆原创 Concentration Ratio
3.4.1 Global Machine Learning in Chip Design 麻豆原创 Concentration Ratio (CR5 and HHI)
3.4.2 Global Top 10 and Top 5 Companies by Machine Learning in Chip Design Revenue in 2023
3.5 Machine Learning in Chip Design Key Players Head office and Area Served
3.6 Key Players Machine Learning in Chip Design Product Solution and Service
3.7 Date of Enter into Machine Learning in Chip Design 麻豆原创
3.8 Mergers & Acquisitions, Expansion Plans
4 Machine Learning in Chip Design Breakdown Data by Type
4.1 Global Machine Learning in Chip Design Historic 麻豆原创 Size by Type (2019-2024)
4.2 Global Machine Learning in Chip Design Forecasted 麻豆原创 Size by Type (2025-2030)
5 Machine Learning in Chip Design Breakdown Data by Application
5.1 Global Machine Learning in Chip Design Historic 麻豆原创 Size by Application (2019-2024)
5.2 Global Machine Learning in Chip Design Forecasted 麻豆原创 Size by Application (2025-2030)
6 North America
6.1 North America Machine Learning in Chip Design 麻豆原创 Size (2019-2030)
6.2 North America Machine Learning in Chip Design 麻豆原创 Growth Rate by Country: 2019 VS 2023 VS 2030
6.3 North America Machine Learning in Chip Design 麻豆原创 Size by Country (2019-2024)
6.4 North America Machine Learning in Chip Design 麻豆原创 Size by Country (2025-2030)
6.5 United States
6.6 Canada
7 Europe
7.1 Europe Machine Learning in Chip Design 麻豆原创 Size (2019-2030)
7.2 Europe Machine Learning in Chip Design 麻豆原创 Growth Rate by Country: 2019 VS 2023 VS 2030
7.3 Europe Machine Learning in Chip Design 麻豆原创 Size by Country (2019-2024)
7.4 Europe Machine Learning in Chip Design 麻豆原创 Size by Country (2025-2030)
7.5 Germany
7.6 France
7.7 U.K.
7.8 Italy
7.9 Russia
7.10 Nordic Countries
8 Asia-Pacific
8.1 Asia-Pacific Machine Learning in Chip Design 麻豆原创 Size (2019-2030)
8.2 Asia-Pacific Machine Learning in Chip Design 麻豆原创 Growth Rate by Region: 2019 VS 2023 VS 2030
8.3 Asia-Pacific Machine Learning in Chip Design 麻豆原创 Size by Region (2019-2024)
8.4 Asia-Pacific Machine Learning in Chip Design 麻豆原创 Size by Region (2025-2030)
8.5 China
8.6 Japan
8.7 South Korea
8.8 Southeast Asia
8.9 India
8.10 Australia
9 Latin America
9.1 Latin America Machine Learning in Chip Design 麻豆原创 Size (2019-2030)
9.2 Latin America Machine Learning in Chip Design 麻豆原创 Growth Rate by Country: 2019 VS 2023 VS 2030
9.3 Latin America Machine Learning in Chip Design 麻豆原创 Size by Country (2019-2024)
9.4 Latin America Machine Learning in Chip Design 麻豆原创 Size by Country (2025-2030)
9.5 Mexico
9.6 Brazil
10 Middle East & Africa
10.1 Middle East & Africa Machine Learning in Chip Design 麻豆原创 Size (2019-2030)
10.2 Middle East & Africa Machine Learning in Chip Design 麻豆原创 Growth Rate by Country: 2019 VS 2023 VS 2030
10.3 Middle East & Africa Machine Learning in Chip Design 麻豆原创 Size by Country (2019-2024)
10.4 Middle East & Africa Machine Learning in Chip Design 麻豆原创 Size by Country (2025-2030)
10.5 Turkey
10.6 Saudi Arabia
10.7 UAE
11 Key Players Profiles
11.1 IBM
11.1.1 IBM Company Detail
11.1.2 IBM Business Overview
11.1.3 IBM Machine Learning in Chip Design Introduction
11.1.4 IBM Revenue in Machine Learning in Chip Design Business (2019-2024)
11.1.5 IBM Recent Development
11.2 Applied Materials
11.2.1 Applied Materials Company Detail
11.2.2 Applied Materials Business Overview
11.2.3 Applied Materials Machine Learning in Chip Design Introduction
11.2.4 Applied Materials Revenue in Machine Learning in Chip Design Business (2019-2024)
11.2.5 Applied Materials Recent Development
11.3 Siemens
11.3.1 Siemens Company Detail
11.3.2 Siemens Business Overview
11.3.3 Siemens Machine Learning in Chip Design Introduction
11.3.4 Siemens Revenue in Machine Learning in Chip Design Business (2019-2024)
11.3.5 Siemens Recent Development
11.4 Google(Alphabet)
11.4.1 Google(Alphabet) Company Detail
11.4.2 Google(Alphabet) Business Overview
11.4.3 Google(Alphabet) Machine Learning in Chip Design Introduction
11.4.4 Google(Alphabet) Revenue in Machine Learning in Chip Design Business (2019-2024)
11.4.5 Google(Alphabet) Recent Development
11.5 Cadence Design Systems
11.5.1 Cadence Design Systems Company Detail
11.5.2 Cadence Design Systems Business Overview
11.5.3 Cadence Design Systems Machine Learning in Chip Design Introduction
11.5.4 Cadence Design Systems Revenue in Machine Learning in Chip Design Business (2019-2024)
11.5.5 Cadence Design Systems Recent Development
11.6 Synopsys
11.6.1 Synopsys Company Detail
11.6.2 Synopsys Business Overview
11.6.3 Synopsys Machine Learning in Chip Design Introduction
11.6.4 Synopsys Revenue in Machine Learning in Chip Design Business (2019-2024)
11.6.5 Synopsys Recent Development
11.7 Intel
11.7.1 Intel Company Detail
11.7.2 Intel Business Overview
11.7.3 Intel Machine Learning in Chip Design Introduction
11.7.4 Intel Revenue in Machine Learning in Chip Design Business (2019-2024)
11.7.5 Intel Recent Development
11.8 NVIDIA
11.8.1 NVIDIA Company Detail
11.8.2 NVIDIA Business Overview
11.8.3 NVIDIA Machine Learning in Chip Design Introduction
11.8.4 NVIDIA Revenue in Machine Learning in Chip Design Business (2019-2024)
11.8.5 NVIDIA Recent Development
11.9 Mentor Graphics
11.9.1 Mentor Graphics Company Detail
11.9.2 Mentor Graphics Business Overview
11.9.3 Mentor Graphics Machine Learning in Chip Design Introduction
11.9.4 Mentor Graphics Revenue in Machine Learning in Chip Design Business (2019-2024)
11.9.5 Mentor Graphics Recent Development
11.10 Flex Logix Technologies
11.10.1 Flex Logix Technologies Company Detail
11.10.2 Flex Logix Technologies Business Overview
11.10.3 Flex Logix Technologies Machine Learning in Chip Design Introduction
11.10.4 Flex Logix Technologies Revenue in Machine Learning in Chip Design Business (2019-2024)
11.10.5 Flex Logix Technologies Recent Development
11.11 Arm Limited
11.11.1 Arm Limited Company Detail
11.11.2 Arm Limited Business Overview
11.11.3 Arm Limited Machine Learning in Chip Design Introduction
11.11.4 Arm Limited Revenue in Machine Learning in Chip Design Business (2019-2024)
11.11.5 Arm Limited Recent Development
11.12 Kneron
11.12.1 Kneron Company Detail
11.12.2 Kneron Business Overview
11.12.3 Kneron Machine Learning in Chip Design Introduction
11.12.4 Kneron Revenue in Machine Learning in Chip Design Business (2019-2024)
11.12.5 Kneron Recent Development
11.13 Graphcore
11.13.1 Graphcore Company Detail
11.13.2 Graphcore Business Overview
11.13.3 Graphcore Machine Learning in Chip Design Introduction
11.13.4 Graphcore Revenue in Machine Learning in Chip Design Business (2019-2024)
11.13.5 Graphcore Recent Development
11.14 Hailo
11.14.1 Hailo Company Detail
11.14.2 Hailo Business Overview
11.14.3 Hailo Machine Learning in Chip Design Introduction
11.14.4 Hailo Revenue in Machine Learning in Chip Design Business (2019-2024)
11.14.5 Hailo Recent Development
11.15 Groq
11.15.1 Groq Company Detail
11.15.2 Groq Business Overview
11.15.3 Groq Machine Learning in Chip Design Introduction
11.15.4 Groq Revenue in Machine Learning in Chip Design Business (2019-2024)
11.15.5 Groq Recent Development
11.16 Mythic AI
11.16.1 Mythic AI Company Detail
11.16.2 Mythic AI Business Overview
11.16.3 Mythic AI Machine Learning in Chip Design Introduction
11.16.4 Mythic AI Revenue in Machine Learning in Chip Design Business (2019-2024)
11.16.5 Mythic AI Recent Development
12 Analyst's Viewpoints/Conclusions
13 Appendix
13.1 Research Methodology
13.1.1 Methodology/Research Approach
13.1.2 Data Source
13.2 Disclaimer
13.3 Author Details
IBM
Applied Materials
Siemens
Google(Alphabet)
Cadence Design Systems
Synopsys
Intel
NVIDIA
Mentor Graphics
Flex Logix Technologies
Arm Limited
Kneron
Graphcore
Hailo
Groq
Mythic AI
听
听
*If Applicable.
