Deep Learning Chipset Market Size, Share, Growth, and Industry Analysis, By Type (Graphics Processing Units (GPUs), Central Processing Units (CPUs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) and Others), By Application (Consumer, Aerospace, Military & Defense, Automotive, Industrial, Medical and Others), Regional Insights, and Forecast From 2023 To 2030

Updated On: 20 November 2023

deep learning chipset market REPORT OVERVIEW

• 
• Request a Free sample to learn more about this report

The global deep learning chipset market size was in USD 4528.7 million in 2022. As per our research, the deep learning chipset market is expected to reach USD 30070 million by 2028, exhibiting a CAGR of 37.1% during the forecast period.

The training process for deep neural networks is sped up by deep learning chipset optimization. They are capable of handling enormous datasets and intricate calculations, and they are more effective than conventional processors in operations like matrix multiplication and convolution. Applying a deep learning model that has been trained to make predictions or judgements on new data is referred to as inference. The inference process can be sped up by learning chipsets, enabling real-time or nearly real-time data processing. This is essential for programs like speech recognition, computer vision, and natural language processing. Energy efficiency is frequently given top priority by learning chipsets, which maximize performance while minimizing power usage. This effectiveness is essential for applications that operate on low-power systems like smartphones, IoT devices, and edge computing platforms.

In order for deep learning chipsets to adapt to certain deep learning models or tasks, they can be modified or reprogrammed. Some chipsets, like FPGAs, provide flexibility in hardware reconfiguration, enabling developers to tailor the design to their particular requirements.

 Popular deep learning software frameworks like TensorFlow, PyTorch, and Caffe are all compatible with deep learning chipsets. For deep learning practitioners, this integration guarantees compatibility and ease of development. Deep learning chipset market have considerably increased the speed and effectiveness of deep learning tasks, revolutionizing the field of artificial intelligence. They have made it possible for developments in a variety of fields, including autonomous cars, natural language processing, computer vision, and recommendation systems.

COVID-19 Impact

"Disruption in Supply Chain to Hinder Market Growth"

International supply networks, especially those in the semiconductor industry, have been disrupted by the pandemic. In order to meet the demand for deep learning chipsets and sustain manufacturing levels, many chip manufacturers encountered difficulties. Delays in manufacturing and delivery have been caused by factory closures, a smaller staff, and transportation issues. Artificial intelligence (AI) and deep learning technologies have become more widely used as a result of the pandemic. As businesses and organizations attempted to create AI-powered solutions for tasks like healthcare diagnostics, remote monitoring, and automation, this fueled demand for deep learning chipset. The supply chain was further strained as a result of the increase in demand. Many businesses and academic institutions switched to remote work settings as a result of lockdowns and social isolation policies. The creation and use of deep learning were impacted by this change.

LATEST TRENDS

"Increased Number of Processing Cores to Augment Market Growth"

Chipsets for deep learning are always working to improve their performance capabilities. The chip architecture must be enhanced, the number of processing cores increased, and the chip's design must be optimised for deep learning activities. To tackle the intense processing needs of deep learning algorithms, many businesses are creating specialised deep learning accelerators. Because these accelerators are designed for matrix operations and neural network computations, training and inference durations are shortened and made more effective. Mixed-precision computing techniques are being included into learning chipsets to increase performance and energy efficiency. Chipsets can conduct calculations more quickly while using less power by using lower precision data formats, such as half-precision (16-bit) or even lower, for some operations.

deep learning chipset market SEGMENTATION

• 
• Request a Free sample to learn more about this report

• By Type

Based on type market is classified as graphics processing units (GPUs), central processing units CPUs.

• By Application

Based on application market is classified as Consumer, Aerospace, Military & Defence, Automotive, Industrial, Medical and Others.

DRIVING FACTORS

"Creation Of Specialised Chipsets to Provide Impetus to the Market"

Deep learning has become a key tool in a number of disciplines, including computer vision, natural language processing, speech recognition, and recommendation systems. The creation of specialised chipsets has been prompted by the rising demand for deep learning applications and the requirement for quicker and more effective processing. Deep learning models frequently entail intricate calculations and extensive matrix operations, which need a lot of processing power. The performance required for these activities may be above the capabilities of conventional central processing units (CPUs), necessitating the use of specialised hardware accelerators like deep learning chipsets.

"High Performance and Energy Efficiency to Enhance Market Growth"

Chipsets for deep learning attempt to balance high performance and energy efficiency. Energy consumption becomes a major concern as deep learning models grow in complexity and size. Deep learning workloads are best performed by specialised chipsets, which are made to maximise computational efficiency, save battery usage, and improve performance. The particular needs of deep neural networks might not be well suited by conventional general-purpose computing architectures. Convolutional operations, matrix multiplications, and activation functions are just a few of the neural network computations that learning chipsets are designed to speed up. There is a growing demand for effective and potent deep learning capabilities at the network edge due to the emergence of edge computing and Internet of Things (IoT) devices.

RESTRAINING FACTORS

"Incapacity Handling Bigger and More Complicated Networks to Impede Market Expansion"

Deep learning models, in particular deep neural networks, demand a lot of computing and processing capacity. These models' intricacy can put a load on current deep learning chipsets, preventing them from handling bigger and more complicated networks. For deep learning models to store weights, activations, and intermediate outputs, a lot of memory is frequently needed. Performance may be hampered by chipset memory restrictions that affect the size of deployable models and the speed of memory access. When working with massive neural networks and data-intensive tasks, learning chipsets can use up a lot of power. High power consumption can make them impractical for use in mobile and edge devices with constrained power supplies and raise the cost of running data centres.

deep learning chipset market REGIONAL INSIGHTS

• 
• Request a Free sample to learn more about this report

"North America to Dominate the Market Due to its Strong Environment for Research"

The United States in particular has been a prominent centre for learning chipset development in North America. Major technology firms, academic institutions, and start-ups with a substantial impact on the industry call it home. Deep learning chipset innovation has been particularly active in Silicon Valley, where organisations like NVIDIA have played a key role. The area is a major player in the learning chipset industry thanks to its strong environment for research, development, and funding. Deep chipsets have experienced fast growth and innovation, particularly in the Asia Pacific area, particularly in China and South Korea. Chinese businesses have invested a lot of money in creating their own learning chipsets, including Huawei, Alibaba, and Baidu. Learning chipset research and development have increased significantly as a result of the region's strategic emphasis on artificial intelligence (AI). Furthermore, South Korea has been making investments in semiconductor and AI technology, with firms like Samsung and SK Hynix making strides in these fields.

KEY INDUSTRY PLAYERS

"Key Players Focus on Partnerships to Gain a Competitive Advantage"

Prominent market players are making collaborative efforts by partnering with other companies to stay ahead of the competition. Many companies are also investing in new product launches to expand their product portfolio. Mergers and acquisitions are also among the key strategies used by players to expand their product portfolios.

List of Market Players Profiled

• NVIDIA (U.S.)
• Intel (U.S.)
• IBM (U.S.)
• Qualcomm (U.S.)
• CEVA (France)
• KnuEdge (U.S.)
• AMD (U.S.)
• Xilinx (China)

REPORT COVERAGE

The report anticipates a detailed analysis of the global market size at the regional and national level, the ssegmentation market growth and market share. The prime objective of the report is to help user understand the market in terms of definition, market potential, influencing trends, and the challenges faced by the market. Aanalysis of sales, the impact of the market players, recent developments, opportunity analysis, strategic market growth analysis, territorial market expansion, and technological innovations are the subject matter explained in the report.