Global Machine Learning Chip Market to Garner $37.85 Billion by 2025, at 40.8% CAGR

Rise in number of machine learning application, increase in trend of artificial intelligence, and emergence of quantum computing have boosted the growth of the global machines learning chip market. The market across the North America region held the largest share in 2018, accounting for more than one-third of the market. However, the multi-chip module segment is estimated to register the fastest CAGR of 44.9% during the forecast period.


Portland, Feb. 18, 2020 (GLOBE NEWSWIRE) -- According to the report published by Allied Market Research, the global machine learning chip market accounted for $3.46 billion in 2018 and is projected to reach $37.85 billion by 2025, registering a CAGR of 40.8% from 2018 to 2025.

Emergence of quantum computing, growth in number of machine learning applications, and rising trend of artificial intelligence (AI) have boosted the growth of the global machine learning chip market. However, the dearth of skilled workforce and AI phobia hamper the market growth. On the contrary, increase in demand for smart homes & smart cities, rise in efforts to make more human-like robots, and popularity of IoT across the world are expected to create lucrative opportunities in the near future.

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The global machine learning chip market is divided on the basis of chip type, technology, industrial vertical, and geography. Based on chip type, the market is divided into GPU, ASIC, FPGA, CPU, and others. The GPU segment dominated the market, contributing nearly one-third of the market. However, the ASIC segment is projected to manifest the fastest CAGR of 47.0% during the forecast period.

Based on technology, the market is bifurcated into system-on-chip (SoC), system-in-package (SIP), multi-chip module, and others. The SoC segment held the largest share in 2018, contributing more than half of the market. However, the multi-chip module segment is estimated to register the fastest CAGR of 44.9% during the forecast period.

Based on industry vertical, the market is segmented into media & advertising, BFSI, IT & telecom, retail, healthcare, automotive & transportation, and others. The BFSI segment held the lion’s share in 2018, contributing more than one-fifth of the market. However, the automotive & transportation segment is projected to portray the fastest CAGR of 45.6% during the forecast period.

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The global machine learning chip market is analyzed across various regions such as North America, Europe, Asia-Pacific, and LAMEA. The market across the North America region held the largest share in 2018, accounting for more than one-third of the market. However, the market across the Asia-Pacific region is anticipated to register the fastest CAGR of 47.1% during the forecast period.

The report provides an in-depth analysis of the major market players including Google Inc., AMD (Advanced Micro Devices), NVIDIA, Intel Corporation, Bitmain Technologies, Baidu, Amazon, Qualcomm, Samsung, and Xilinx.

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Allied Market Research (AMR) is a full-service market research and business-consulting wing of Allied Analytics LLP based in Portland, Oregon. Allied Market Research provides global enterprises as well as medium and small businesses with unmatched quality of "Market Research Reports" and "Business Intelligence Solutions." AMR has a targeted view to provide business insights and consulting to assist its clients to make strategic business decisions and achieve sustainable growth in their respective market domain.

We are in professional corporate relations with various companies and this helps us in digging out market data that helps us generate accurate research data tables and confirms utmost accuracy in our market forecasting. Each and every data presented in the reports published by us is extracted through primary interviews with top officials from leading companies of domain concerned. Our secondary data procurement methodology includes deep online and offline research and discussion with knowledgeable professionals and analysts in the industry.


            

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