Deep Learning Chip Market to Garner $29.37 Billion 2025, at 39.9% CAGR: AMR

Increase in demand for smart homes & smart cities, rise in investment in AI startups, growth in number of AI applications, and emergence of quantum computing are expected to propel the growth of the global deep learning chip market. The market across the North America region held the lion’s share in 2018, contributing more than one-third of the market. On the other hand, the SoC segment held the largest share in 2018, contributing more than half of the market.


Portland, March 30, 2020 (GLOBE NEWSWIRE) -- According to the report published by Allied Market Research, the global deep learning chip market was pegged at $2.81 billion in 2018 and is estimated to reach $29.37 billion by 2025, registering a CAGR of 39.9% CAGR from 2018 to 2025.

Rise in demand for smart homes & smart cities, increased in investment in AI startups, emergence of quantum computing, and growth in number of AI applications have boosted the growth of the global deep learning chip market. However, dearth of skilled workforce hampers the market. On the contrary, increased adoption of deep learning chips in developing regions and development of smarter robots are expected to create lucrative opportunities in the near future.

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The global deep learning chip market is divided on the basis of chip type, technology, industry verticals, and geography. Based on chip type, the market is segmented into GPU, ASIC, FPGA, CPU, and others. The GPU segment dominated the market, contributing more than one-fourth of the market. However, the ASIC segment is projected to register the fastest CAGR of 46.1% during the forecast period.

On the basis of technology, the market is divided into system-on-chip (SoC), system-in-package, 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 manifest the fastest CAGR of 44.0% during the forecast period.

Based on industrial verticals, the market is bifurcated into media & advertising, BFSI, it & telecom, retail, healthcare, automotive & transportation, and others. The BSFI segment held the largest share in 2018, contributing more than one-fifth of the market. However, automotive & transportation segment is expected to register the fastest CAGR of 44.7% during the forecast period.

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The global deep 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 lion’s share in 2018, contributing more than one-third of the market. However, the Asia-Pacific region is projected to propel the fastest CAGR of 44.5% during the forecast period.

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

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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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