Machine Learning Chip Market Major players: AMD (Advanced Micro Devices), Google, Inc., Intel Corporation, NVIDIA, Baidu, Bitmain Technologies, Qualcomm, Amazon, Xilinx, Samsung

According to a recent report published by Allied Market Research, titled, Machine Learning Chip Market by Chip type, Technology, and Industry vertical: Global Opportunity Analysis and Industry Forecast, 2018-2025, the global machine learning chip market was valued at $2,425.6 million in 2017, and is projected to reach $37,849.8 million by 2025, registering a CAGR of 40.8% from 2018 to 2025.

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At present, North America dominates the market, followed by Europe. In 2017, the U.S. dominated the North America market, and the UK led the overall market in Europe. While, in Asia-Pacific, China currently dominates the market.

The trend in artificial intelligence (AI), use of machine learning in numerous applications and emergence of quantum computing are the factors which increase the demand for machine learning chip market. In addition, the development of autonomous robots that can control themselves without human intervention is anticipated to provide potential growth opportunities for the market. However, dearth of skilled workforce and AI phobia are the major restraints of the market. Moreover, increase in demand for smart homes & cities, rise in efforts to make more human-like robots and popularity of IoT across the globe are expected to create tremendous opportunities for the market expansion.

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Key Findings of the Machine Learning Chip Market:
Based on chip type, the GPU segment dominated the global machine learning chip market in 2017. However, the ASIC segment is anticipated to overtake the GPU segment in future, in terms of revenue.
North America held the major market share in 2017.
Based on industry vertical, the BFSI segment dominated the global machine learning chip market in 2017. However, the others segment is expected to grow at the highest CAGR during the forecast period.
Asia Pacific is anticipated to exhibit the highest CAGR during the forecast period.

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