LOS ANGELES, Jan. 27, 2020 (GLOBE NEWSWIRE) — The global deep learning market is expected to grow at a CAGR of around 51.1% over the forecast period 2019 to 2026 and reach the market value of over US$ 56,427.2 million by 2026.
North America held the major share of the global deep learning market in 2018. The well-established technology infrastructure of the region is supporting market growth. The high demand for deep learning application in healthcare, aerospace & defense, automotive, and telecommunications sector is contributing to the market growth.
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Hardware components accounted for the major share in 2018 and the t is also anticipated to maintain its dominance during the forecast timeframe 2019 to 2026. The hardware is required to increase the efficiency of deep learning applications is particularly supporting segment growth.
On the basis of application, the image recognition segment held the dominating share in the deep learning market. The increasing demand for optical character recognition and for identifying and detecting an object or a feature in a digital image or video is particularly accelerating the market value.
Security segment accounted for the major share by end-user in 2018. The segment is particularly gaining growth due to rising number of cyber attacks in various industry verticals. The increasing dependency of organizations on the data create need to figure out patterns from the available data for efficient decision making process. For instance, Oracle is developing Financial Crime and Compliance Studio (FCC Studio) with the help of deep learning and graph analytics. The new development is intended to detect repetitive patterns in graphs of data of individuals particularly to assist know-your-customer (KYC) and anti-money laundering (AML) activities.
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Some of the leading competitors are Amazon Web Services (AWS), Google, IBM, Intel, Micron Technology, Microsoft, Nvidia, Qualcomm, Samsung Electronics, Sensory Inc., Skymind, Xilinx, AMD, General Vision, Graphcore, Mellanox Technologies, Huawei Technologies, Fujitsu, Baidu, Mythic, Adapteva, Inc., and Koniku. Deep learning companies have announced mergers and acquisitions to expand their position in the deep learning industry. Major players are also moving into new regions or advanced technologies. For instance, Intel has acquired artificial intelligence chipmaker Habana Labs in 2019 for approx. US$ 2 billion to gain a competitive advantage and also to enhance its deep learning AI capability by increasing its portfolio.
Some of the key observations regarding deep learning industry include:
- Amazon has officially launched an open-source toolkit on 9th January 2020. The toolkit name AutoGluon is designed for automated machine learning for providing ease to software developers in taking advantage of deep learning models. The new kit is designed for both machine learning experts and beginners to achieve strong, predictive performance in their application.
- Salk Institute researchers have developed a new microscopy approach for faster brain imaging by 16 times. The researchers have used data from the Texas Advanced Computing Center (TACC), the University of Texas to train their deep learning system.
- KaiKuTek, a Taiwan based startup led by Taiwan Tech Arena (TTA) has showcased innovative solutions in January at Consumer Electronics Show (CES) 2020. KaiKuTek has developed world-first 60GHz gesture recognition SoC that combines deep learning algorithms, and AI accelerators named mmWave.
- QuEST Global has demonstrated Deep Learning driven Advanced Driver Assistance Systems (ADAS) at CES 2020. The new deep learning model is intended to enhance ADAS by improving the accuracy in the detection of traffic signs, pedestrians and traffic.
- Amazon has released Deep Java Library (DJL) at reinvent 2019. The new development is an open-source library with Java APIs particularly to simplify training, testing, deploying, and making predictions with the help of deep-learning models.
- MATLAB software enables the simple and agile development of advance deep learning networks with higher speed, less memory and better results in 2020 with its new version.
- Intel has shipped the industry’s first silicon for neural network processor in 2017 named Intel Nervana Neural Network Processor (NNP). The new processor is intended to develop entirely new classes of artificial intelligence applications to maximize the amount of data processed and enable customers to find greater insights with deep learning models.
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