The market for deep learning chipsets is anticipated to expand at a CAGR of 24.7% during the forecast period of 2024 and 2032, propelled by technological advancements, rising demand for high-performance computation, and increased adoption of AI in key industries. Although there are challenges to data security and privacy, it is anticipated that the market will surmount these barriers using ongoing innovation. The proliferation of artificial intelligence (AI) has piqued the interest of stakeholders spanning numerous vertical industries. Artificial intelligence has advanced substantially, from neural networks to the deep learning architectures of today. In recent years, the demand for deep learning chipsets has increased substantially across numerous industries, including consumer electronics, healthcare, aerospace & defense, and automotive. It is expected that this development will significantly influence the expansion of the deep-learning chipset industry in the coming years. The widespread adoption of deep learning chipsets is primarily driven by the enormous volumes of data required to implement deep learning and machine learning models. At this time, advancements in technology are enabling the development of sophisticated and powerful deep-learning chipsets. An increasing number of consumer electronics, such as smart speakers, smartphones, augmented reality/virtual reality (AR/VR) headwear, and numerous other devices that require AI processing, are integrating deep learning chipsets. Several companies operating in the deep learning chipset sector are currently focused on advancing fabrication and deep learning chipset design innovations to streamline the production of state-of-the-art devices. Geographically, the Asia-Pacific region and North America are of significant importance, as they each make distinct contributions to the dynamics of the market.
Key Market Drivers
Developments in the Field of GPU Technology: The progression of graphics processing units (GPUs) emerges as a principal catalyst in the market for deep learning chips. Significant developments in GPU technology have facilitated parallel processing capabilities that are highly suitable for the intricate computations associated with deep learning tasks. The increasing need for high-performance GPUs in various sectors indicates that their integration has become crucial in expediting deep learning algorithms. Prominent corporations, including NVIDIA, have consistently exhibited leadership in the realm of GPU innovation by delivering potent solutions that effectively address the increasing computational requirements of deep learning applications.
The Significance of Application-Specific Integrated Circuits (ASICs): The proliferation of Application-Specific Integrated Circuits (ASICs) becomes a decisive factor that influences the deep learning chipset landscape. Specifically engineered for such operations, ASICs execute deep learning algorithms with an unprecedented degree of efficiency, surpassing the capabilities of general-purpose processors. Prominent corporations, including Google, have designed and implemented ASICs, exemplified by the Tensor Processing Unit (TPU), which has had a revolutionary effect on the inference of deep learning models. The inclination towards ASICs is motivated by the requirement for energy-efficient, optimized solutions capable of managing the complexities of deep neural networks in a variety of applications.
Increased Demand in the Industrial and Automotive Sectors: The demand for high-performance computing solutions has been significantly propelled by the industrial and automotive sectors, which have emerged as key drivers of the deep learning chip market. Chipsets for deep learning are utilized extensively in the fields of predictive maintenance, quality control, and industrial automation. These chipsets provide power to autonomous driving technologies and advanced driver assistance systems (ADAS) in the automotive industry. The expanding market is supported by the increasing incorporation of deep learning capabilities into intelligent vehicles and manufacturing processes. Intel and NVIDIA, among others, are making substantial contributions to the implementation of deep learning chipsets in the automotive and industrial sectors using strategic partnerships and advancements in their products.
Difficulties in FPGA Integration: Restraint Notwithstanding the general expansion, the incorporation of Field Programmable Gate Arrays (FPGAs) presents obstacles in the market for deep learning chips. Despite their adaptability, FPGAs are frequently accompanied by increased development expenses and intricacies. In contrast to their capacity for customization, FPGAs require specialized knowledge for their deployment and programming. The integration of FPGAs into deep learning solutions by companies like Xilinx encounters challenges that prevent their widespread adoption on account of these complexities. The market continues to be constrained by the obstacles associated with FPGA integration, which necessitates the collaboration of industry stakeholders.
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Key Market Segmentation Analysis
Market Segmentation by Type: Graphics Processing Units (GPUs), Central Processing Units (CPUs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), and other subtypes comprise the deep learning chipset market. GPUs surpassed all other types in revenue generation in 2023, owing to their extensive utilization across diverse sectors including gaming, data centers, and AI processing. ASICs are anticipated to experience the highest Compound Annual Growth Rate (CAGR) from 2024 to 2032, according to projections. As the demand for application-specific chipsets increases, the ASIC market is anticipated to be propelled by the rise in prominence of firms such as Bitmain and Canaan Creative.
Market Segmentation: Compute Capacity: The deep learning chipset market is divided into two categories, namely high capacity and low capacity, concerning compute capacity. Revenue-wise, high compute capacity chipsets dominated in 2023, primarily as a result of their widespread implementation in complex AI and ML applications that demand substantial processing power. During the forecast period, low compute capacity chipsets will, however, account for the maximum CAGR. The expansion can be ascribed to the mounting need for peripheral computing and Internet of Things (IoT) devices, in which extensive use is made of power-efficient chipsets featuring reduced computing capacities.
Market segmentation is accomplished at the end user level, with various industries such as automotive, consumer electronics, healthcare, aerospace & defense, and others comprising this sector. The industrial sector generated the most revenue in 2023, utilizing deep learning chipsets for applications including quality control, robotics, and automation. Conversely, the healthcare industry is anticipated to experience the highest CAGR throughout the forecast period. The greater incorporation of artificial intelligence into personalized medicine, diagnostics, and medical imaging is fueling the demand for deep learning chipsets in the healthcare industry.
North America Continues to be the Global Leader
Geographically, the market for deep learning chipsets demonstrates a variety of trends. The market was dominated by North America in 2023 in revenue percentage. This dominance was attributed to the presence of significant industry actors, considerable investments in AI research, and the widespread adoption of advanced technologies. Conversely, the Asia-Pacific region is anticipated to experience the most substantial CAGR over the projected timeframe. The anticipated development of the region is financed in part by governmental initiatives that encourage technological advancements, the expanding use of artificial intelligence in emerging economies, and swift industrialization. It is anticipated that Europe, due to its emphasis on research and development, will exert a substantial influence on the dynamics of the market.
Competition In the Market Will Intensify Throughout the Forecast Period
Prominent industry participants, including NVIDIA, Intel, AMD, Qualcomm, and IBM, have positioned themselves as leaders in the deep learning chipset sector. Diverse strategies are utilized by these businesses to preserve their market positions. Revenue-wise, NVIDIA dominated in 2023, as its control over the gaming, data center, and automotive industries propelled it to the top spot. Nevertheless, Intel is anticipated to emerge as a formidable competitor from 2024 to 2032 by capitalizing on its progress in CPU and GPU technologies.
As a result of its strategic emphasis on AI-driven solutions and acquisition of AI chipmaker Habana Labs, Intel is well positioned for substantial expansion. AMD, known for its commitment to delivering high-performance graphics processing units (GPUs), is expected to observe significant revenue expansion throughout the projected timeframe. Tailoring its Snapdragon platforms for mobile devices to the rising demand for AI processing in smartphones and tablets, Qualcomm's entry into the AI market introduces an additional factor to the competitive landscape.
IBM's proficiency in artificial intelligence and quantum computation is poised to significantly influence the trajectory of the market. The organization's focus on creating specialized chipsets for healthcare applications corresponds to the expected expansion of the industry. In general, the competitive landscape is characterized by a dynamic nature, as participants strategically position themselves to take advantage of the changing needs across various sectors.
