Deep Learning Chipset Market is valued at USD 2411.45 Million in 2020 and expected to reach USD 5652.15 Million by 2027 with the CAGR of 37% over the forecast period.
Deep learning chips are customized silicon chips that assimilate Ai Technology and machine learning. Deep learning and machine learning, which are the sub-sets of Artificial intelligence (AI) sub-sets, are used in carrying out AI related tasks. A single-chip processor generates lighting effects and transforms objects each time a 3D scene is redrawn. Or a graphic processing unit turns out to be very expressive and efficient when applied to computation styles needed for neural nets. This in turn fuels the growth of the market for deep learning chipsets. For Example, NVidia GPU's, which are most popular nowadays for deep learning, can do both training and inference. Some other examples are Graphcore IPU, Google TPU V3, Cerebras, etc. OpenAI has great analysis displaying the recent increase in compute required for training large networks.
Global Deep Learning Chipset market report is segmented on the basis of Product type and application. Based upon Product type Deep Learning Chipset is classified into Graphics Processing Units (GPUs), Central Processing Units (CPUs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) and others. Based upon application Deep Learning Chipset is classified into Consumer, Aerospace, Military and Defense, Automotive, Industrial, Medical and Others.
The regions covered in this global Deep Learning Chipset market report are North America, Europe, Asia-Pacific and Rest of the World. On the basis of country level market of Deep Learning Chipset is sub divided into U.S., Mexico, Canada, UK, France, Germany, Italy, China, Japan, India, South East Asia, GCC, Africa, etc.
Global Deep Learning Chipset market Report covers prominent players are like
News: - 18/05/2020, Recently, NVIDIA CEO and Co-Founder Jensen Huang introduced GPU-based NVIDIA A100. Huang made the announcement from the kitchen of his California home along with discussions on important new software technologies such as NVIDIA Jarvis and recent Mellanox acquisition. NVIDIA A100 is believed to be the first GPU based on the NVIDIA Ampere architecture with 54 billion transistors. The NVIDIA A100 Tensor Core GPU is said to deliver unprecedented acceleration at every scale for AI, data analytics, including high-performance computing (HPC) to tackle highly complex processing challenges.
Increased adoption of cloud based technology and deep learning usage in big data analytics are the factors driving the growth of the deep learning chipset market. Cloud assumption is a strategy used by enterprises to improve the scalability of Internet-based database capabilities while reducing cost and risk. Cloud-based software and platforms help companies benefit from AI, even if they lack the expertise to build and train systems, or to manage data on their own. Deep learning technology has arrived many industries around the world and is accomplished through applications like computer vision, speech synthesis, voice recognition, machine translation, drug discovery, game play and robotics. According to Cloud Computing Statistics, 90% of companies use some type of cloud service. 80% of enterprises use Amazon Web Services as their primary cloud platform. Also 77% of enterprises have at least one application or a portion of it in the cloud. With machine learning technologies, computers can be taught to analyze data, identify hidden patterns, make classifications and predict future outcomes. Deep learning is a subset of machine learning based on a conceptual model of the human brain called neural networks. However, low awareness of chipset besides CPU is likely to hamper the growth of the deep learning chipset market during the forecast period. Furthermore, Popularity of Internet of Things (IoT) and increase in demand for automated devices are anticipated to provide lucrative growth opportunities for the key players in the deep learning chipset market.
North America is expected to dominate the growth of Deep Learning Chipset Market, due to increase in demand for smart homes and Smart cities, rise in investments in AI startups, emergence of quantum computing, growth in the number of AI development of smarter robots. The majority (69%) of U.S. households now own at least one smart home device, based on a new study. That translates to 83 million households and of those, 18% or 22 million homes, own more than one smart home product, according to the study by the Consumer Technology Association (CTA). Europe is second largest region for the growth of Deep Learning Chipset Market, due to increased adoption of cloud based technology.
|Historical data||2015 - 2020|
|Forecast Period||2021 - 2027|
|Market Size in 2020:||USD 2411.45 Million|
|Base year considered||2020|
|Forecast Period CAGR %:||37 %|
|Market Size Expected in 2027:||USD 5652.15 Million|
|Tables, Charts & Figures:||175|
|Key Players/Companies||NVIDIA, Intel, IBM, Qualcomm, CEVA, KnuEdge, AMD, Xilinx, ARM, Google, Graphcore, TeraDeep, Wave Computing, BrainChip , Others|
|Segments Covered||By Product Type, By Application|
|Regional Analysis||North America, U.S., Mexico, Canada, Europe, UK, France, Germany, Italy, Asia Pacific, China, Japan, India, Southeast Asia, South America, Brazil, Argentina, Columbia, The Middle East and Africa, GCC, Africa, Rest of the Middle East and Africa|
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