Deep Learning Chipset Market Worth US$ 35.2 Bn by 2027

The artificial intelligence (AI) wave has captivated the attention of stakeholders operating in an array of industry verticals. Evolving from neural networks to present-day deep learning architectures, AI has come a long way. The soaring demand for deep learning across a host of industrial sectors, including healthcare, aerospace & defense, automotive, and consumer electronics has played an imperative role in boosting the demand for deep learning chipsets in recent years - a trend that is likely to play a key role in the expansion of the deep learning chipset market in the coming years. The uptake of deep learning chipsets is primarily driven by high volumes of data required to run deep learning and machine learning models.

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In the current scenario, technological advancements are enabling the development of powerful and cutting-edge deep learning chipsets. Deep learning chipsets are increasingly being used in a range of consumer electronic items, including augmented reality/virtual reality (AR/VR) headsets, smart speakers, smartphones, and a host of other devices that require AI processing. Several companies operating in the deep learning chipset market are focusing on introducing innovations in fabrication and deep learning chipset designs to enable the production of state-of-the-art devices.

Moreover, progress in the AI domain is expected to streamline and improve a machine's capacity to carry out cognitive functions linked with humans, including reasoning, learning, and perceiving. Due to these factors, along with widening applications of AI, the deep learning chipset market is expected to reach a value of ~US$ 35.2 Bn by the end of 2027, a five-fold growth from ~US$ 6.4 Bn in 2019.

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With the growing need to streamline large volumes of data (training) and computing answers (interference), demand for highly sophisticated deep learning chipsets witnessed unprecedented growth. A large amount of deep learning chipsets was developed for usage across data centers worldwide. However, the trend is gradually expected to shift as stakeholders in the deep learning chipset market are expecting a majority of processing to be carried out at the edge of and closer to sensor arrays.

AI companies are investing resources toward the development of deep learning chipset-based technologies. Although the adoption of deep learning chipsets will continue in data centers, AI processing is expected to be employed in next-generation devices, including security cameras, drones, smartphones, etc.

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The first-generation of graphic processing units were primarily developed for desktop gaming. However, the trend witnessed a tectonic shift, particularly in the last decade, as new iterations of graphic processing units were more inclined toward high-resolution images and AI. The significant demand for graphic processing units can be largely attributed to significant progress in low-power technology.

For instance, the Google Brain project, which was initiated in 2011, analyzed millions of images from platforms, such as YouTube to recognize cats. Due to significant progress in technology, the generation of graphical processing units could integrate exceptional graphical capabilities with computational processing components. Furthermore, with continual demand for higher graphical requirements, owing to large volumes of high-resolution images, stakeholders in the deep learning chipset market developed deep learning chipsets with improved functions and capabilities. The demand for graphical processing units was largely influenced by the mounting need for advanced graphical processing. Additionally, as deep learning tasks involve highly complex mathematical computations, the demand for graphics processing units grew at an impressive pace. Within the deep learning chipset market, the graphics processing units (GPUs) segment is estimated to reach a value of ~US$ 2.4 Bn and account for a market share of ~31% in 2020.

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The deep learning chipset market was largely dominated by NVIDIA for a substantial period. However, in recent times, a large number of companies have turned their attention toward the development of highly advanced deep learning chipsets. For instance, in July 2018, IBM launched a new deep learning chipset, which was predominantly designed to perform high-precision learning as well as low-precision inference.

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