The global machine learning chip market reached a value of US$ 5.86 Billion in 2021. Looking forward, the publisher expects the market to reach a value of US$ 33.02 Billion by 2027 exhibiting a CAGR of 31.20% during 2022-2027. Keeping in mind the uncertainties of COVID-19, we are continuously tracking and evaluating the direct as well as the indirect influence of the pandemic on different end use industries. These insights are included in the report as a major market contributor.
Machine learning (ML) refers to a part of artificial intelligence (AI), which operates on experiential learning instead of programming for making decisions. ML chips are installed to improve intellectual property cores and enhance design and tool flows. They also help automate the tuning and routing of devices to improve the power, performance, and area (PPA) results through ML, analytics, and optimization. The hardware infrastructure of ML chips mainly consists of three components, storage, networking, and computing. Some of the standard ML chips include field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), and graphics processing units (GPUs). These chips are used for error prevention and cost-saving purposes across various industries, such as manufacturing, automotive, media and advertising, transportation, and finance.
The widespread utilization of AI in several industry verticals and the emergence of quantum computing are some of the main factors driving the global machine learning chip market growth. A significant expansion in the gaming industry has led to a shift from central processing units (CPUs) to graphics processing units (GPUs) for simultaneously performing various complex tasks, which is further contributing to the growth of the market. Apart from this, the establishment of smart infrastructure in corporate organizations and the commencement of smart city projects are fueling the market growth. The growing number of machine learning (ML) applications and the increasing demand for smart homes are providing an impetus to the market growth. Moreover, the emergence of the Internet-of-Things (IoT) and the rising development of human-like robots to reduce human intervention and errors are some of the other factors creating a positive outlook for the market across the globe.
The publisher provides an analysis of the key trends in each sub-segment of the global machine learning chip market, along with forecasts at the global, regional and country level from 2022-2027. Our report has categorized the market based on technology, chip type and industry vertical.
The competitive landscape of the industry has also been examined along with the profiles of the key players being Advanced Micro Devices Inc., Amazon Web Services Inc. (Amazon.com Inc.), Cerebras Inc., Google LLC, Graphcore, Intel Corporation, International Business Machines Corporation, NVIDIA Corporation, Qualcomm Incorporated, Samsung Electronics Co. Ltd. and Taiwan Semiconductor Manufacturing Company Limited.
Machine learning (ML) refers to a part of artificial intelligence (AI), which operates on experiential learning instead of programming for making decisions. ML chips are installed to improve intellectual property cores and enhance design and tool flows. They also help automate the tuning and routing of devices to improve the power, performance, and area (PPA) results through ML, analytics, and optimization. The hardware infrastructure of ML chips mainly consists of three components, storage, networking, and computing. Some of the standard ML chips include field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), and graphics processing units (GPUs). These chips are used for error prevention and cost-saving purposes across various industries, such as manufacturing, automotive, media and advertising, transportation, and finance.
Machine Learning Chip Market Trends:
The widespread utilization of AI in several industry verticals and the emergence of quantum computing are some of the main factors driving the global machine learning chip market growth. A significant expansion in the gaming industry has led to a shift from central processing units (CPUs) to graphics processing units (GPUs) for simultaneously performing various complex tasks, which is further contributing to the growth of the market. Apart from this, the establishment of smart infrastructure in corporate organizations and the commencement of smart city projects are fueling the market growth. The growing number of machine learning (ML) applications and the increasing demand for smart homes are providing an impetus to the market growth. Moreover, the emergence of the Internet-of-Things (IoT) and the rising development of human-like robots to reduce human intervention and errors are some of the other factors creating a positive outlook for the market across the globe.
Key Market Segmentation:
The publisher provides an analysis of the key trends in each sub-segment of the global machine learning chip market, along with forecasts at the global, regional and country level from 2022-2027. Our report has categorized the market based on technology, chip type and industry vertical.
Breakup by Technology:
- System-on-Chip (SoC)
- System-in-Package
- Multi-chip Module
- Others
Breakup by Chip Type:
- GPU
- ASIC
- FPGA
- CPU
- Others
Breakup by Industry Vertical:
- BFSI
- IT and Telecom
- Media and Advertising
- Retail
- Healthcare
- Automotive
- Others
Breakup by Region:
- North America
- United States
- Canada
- Asia-Pacific
- China
- Japan
- India
- South Korea
- Australia
- Indonesia
- Others
- Europe
- Germany
- France
- United Kingdom
- Italy
- Spain
- Russia
- Others
- Latin America
- Brazil
- Mexico
- Others
- Middle East and Africa
Competitive Landscape:
The competitive landscape of the industry has also been examined along with the profiles of the key players being Advanced Micro Devices Inc., Amazon Web Services Inc. (Amazon.com Inc.), Cerebras Inc., Google LLC, Graphcore, Intel Corporation, International Business Machines Corporation, NVIDIA Corporation, Qualcomm Incorporated, Samsung Electronics Co. Ltd. and Taiwan Semiconductor Manufacturing Company Limited.
Key Questions Answered in This Report:
- How has the global machine learning chip market performed so far and how will it perform in the coming years?
- What has been the impact of COVID-19 on the global machine learning chip market?
- What are the key regional markets?
- What is the breakup of the market based on the technology?
- What is the breakup of the market based on the chip type?
- What is the breakup of the market based on the industry vertical?
- What are the various stages in the value chain of the industry?
- What are the key driving factors and challenges in the industry?
- What is the structure of the global machine learning chip market and who are the key players?
- What is the degree of competition in the industry?
Frequently Asked Questions about the Global Machine Learning Chip Market
What is the estimated value of the Global Machine Learning Chip Market?
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| Report Attribute | Details |
|---|---|
| No. of Pages | 143 |
| Published | March 2022 |
| Forecast Period | 2021 - 2027 |
| Estimated Market Value ( USD | $ 5.86 Billion |
| Forecasted Market Value ( USD | $ 33.02 Billion |
| Compound Annual Growth Rate | 33.4% |
| Regions Covered | Global |
| No. of Companies Mentioned | 11 |
Table of Contents
1 Preface3 Executive Summary11 Value Chain Analysis13 Price Analysis
2 Scope and Methodology
4 Introduction
5 Global Machine Learning Chip Market
6 Market Breakup by Technology
7 Market Breakup by Chip Type
8 Market Breakup by Industry Vertical
9 Market Breakup by Region
10 SWOT Analysis
12 Porters Five Forces Analysis
14 Competitive Landscape
List of Figures
List of Tables
Companies Mentioned
- Advanced Micro Devices Inc.
- Amazon Web Services Inc. (Amazon.com Inc.)
- Cerebras Inc.
- Google LLC
- Graphcore
- Intel Corporation
- International Business Machines Corporation
- NVIDIA Corporation
- Qualcomm Incorporated
- Samsung Electronics Co. Ltd.
- Taiwan Semiconductor Manufacturing Company Limited.
Methodology
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