Deep learning chipset is a part of machine learning methods. It is grounded in the multilayer neural network and is the foundation of real time streaming analytics, cognitive computing, and artificial intelligence. Deep learning constructions such as recurrent neural networks, deep neural networks is applied to the fields which includes natural language processing, machine translation, and others.
Embedding and widespread adoption of deep-learning technology is dependent on the continued miniaturization and commoditization of low-cost hardware technologies that accelerates algorithmic processing. The technology is further anticipated to gain importance among researchers and key players for its application in artificial intelligence capabilities in computer vision areas, natural language processing, and speech & image recognition. Solution providers are resorting to collaborations and partnerships to enter the deep learning market and gain competitive edge over the other players.
The deep learning chipset technology drives the progress of the artificial intelligence. Immediate improvement in parallelization, high computing power, and efficient information storage capacity have contributed to the deep learning technology in industries such as healthcare and automotive. Some industry experts predict that technology can built new business designs through efficient predictive capabilities and can transform business processes. The increasing importance of GPUs applications is leading to increased adoption of the technology in scientific disciplines such as data science and deep learning which is expected to boost the market growth.
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Significant improvements in advancements in deep learning chipsets and machine learning algorithms are driving the market growth. However when compared to field programmable gate arrays, lack of the ability to accommodate hardware changes as neural networks and algorithms evolve, high power consumption lead to fall in the market growth. However deep learning chipset is also expected to show higher penetrate rate as the intelligence that is driving a new generation of wearable that would help disabled people to hear, see, and sense their surroundings. The technology is expected to find its way into devices such as microphones, incorporate embedded cameras, and Internet of Things resulting into the further growth of the market during the forecast period.
The deep learning chipset market is segmented on the basis of type, application, and region. On the basis of type, the market is segmented into application specific integrated circuits, graphics processing units, central processing units, and field programmable gate arrays. The deep learning chipset market is led by graphics processing units. The importance of graphic processing units has gone up with the emergence of deep learning, while training it was found that GPUs are faster than CPUs. On the basis of Application is the global market is segmented into aerospace, military & defense, automotive, consumer, industrial, medical and others.
In healthcare, deep learning chipset is applied to analyze medical images. This is an area where deep learning chipset is used on medical images for automated tumor detection. In the automotive industry deep learning chipset based on neural network computing, unassisted cloud-based crowd learning and parallel processing are driving key innovations. Its application areas include speech recognition and machine vision and both have huge relevance for automotive. Geographically deep learning chipset market is segmented into Europe, North America, South America, Middle East & Africa, and Asia-Pacific. The deep learning chipset market in North America is expected to expand at highest growth rate. North America is acquiring deep learning technology within the organizations for safeguarding content from data breaches, piracy, web and severe data losses, cyber-attacks, and network threat security.
Some of the key players in the deep learning chipset market are Intel Corporation, NVIDIA Corporation, IBM Computer Manufacturing Company, Qualcomm Technologies, Inc., Advanced Micro Devices, Inc., XILINX INC., Arm Limited, Google LLC, GRAPHCORE, TeraDeep Inc., Wave Computing, and BrainChip Holdings Ltd. among others.
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