Deep Learning Chipset Market Expansion 2024-2033: Growth Drivers and Dynamics

The deep learning chipset global market report 2024from The Business Research Company provides comprehensive market statistics, including global market size, regional shares, competitor market share, detailed segments, trends, and opportunities. This report offers an in-depth analysis of current and future industry scenarios, delivering a complete perspective for thriving in the industrial automation software market.

Deep Learning Chipset Market, 2024The deep learning chipset global market report 2024

Market Size –

The deep learning chipset market size has grown exponentially in recent years. It will grow from $7.43 billion in 2023 to $9.47 billion in 2024 at a compound annual growth rate (CAGR) of 27.4%. The growth in the historic period can be attributed to the growing need to streamline large volumes of data, the rise of cloud computing, the development of AI-driven applications, government investments, and the evolution of AI frameworks and libraries.
The deep learning chipset market size is expected to see exponential growth in the next few years. It will grow to $25.17 billion in 2028 at a compound annual growth rate (CAGR) of 27.7%. The growth in the forecast period can be attributed to growth of autonomous systems, the emergence of 5g technology, increasing focus on energy efficiency, rising adoption of IoT, and rising demand for automobiles. Major trends in the forecast period include advances in neuromorphic computing, customization of AI hardware, focus on energy-efficient AI solutions, advancement in AI-powered healthcare devices, and adoption of cloud-based technology.

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Scope Of Deep Learning Chipset MarketThe Business Research Company’s reports encompass a wide range of information, including:

1. Market Size (Historic and Forecast): Analysis of the market’s historical performance and projections for future growth.

2. Drivers: Examination of the key factors propelling market growth.

3. Trends: Identification of emerging trends and patterns shaping the market landscape.

4. Key Segments: Breakdown of the market into its primary segments and their respective performance.

5. Focus Regions and Geographies: Insight into the most critical regions and geographical areas influencing the market.

6. Macro Economic Factors: Assessment of broader economic elements impacting the market.

Deep Learning Chipset Market Overview

Market Drivers –

The adoption of Internet of Things (IoT) devices is growing due to advancements in technology and increased demand for smart, connected systems, which in turn is driving the deep learning chipset market. These chipsets are essential for processing vast amounts of data generated by IoT devices, improving AI capabilities through real-time decision-making. According to a 2022 report by Ericsson, global IoT connections reached 13.2 billion in 2022 and are projected to rise to 34.7 billion by 2028, further boosting the demand for deep learning chipsets in the coming years.

Market Trends –

Major companies operating in the deep-learning chipset market are focusing on developing advanced products, such as deep-learning processors, to enhance computational efficiency and improve processing speeds for complex AI applications. Deep learning processors are designed to accelerate the computational tasks involved in deep learning and utilize neural networks with multiple layers to analyze large amounts of data. For instance, in May 2022, Habana Labs Ltd., a US-based deep learning and AI processor chip manufacturing company, launched Habana Gaudi2 Training and Habanab Greco, second-generation deep learning processors. Habana Gaudi2 has tremendous performance improvements, achieving up to 2x throughput compared to Nvidia’s A100 GPU. It has 24 tensor processor cores, a substantial increase over its predecessor, and 96 GB of HBM2E memory, triple the previous capacity. It features 24 100 Gigabit RDMA connections, allowing for scalable training settings and reducing reliance on proprietary technology. Habanab Greco is expected to make significant speed gains over its predecessor, comparable to the advancements observed in Gaudi 2.

The deep learning chipset market covered in this report is segmented –

1) By Type: Graphics Processing Units (GPUs), Central Processing Units (CPUs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), Other Types
2) By Technology: System-On-Chip (SOC), System-In-Package (SIP), Multi-Chip Module, Other Technologies
3) By Compute Capacity: High, Low
4) By End-User Industry: Healthcare, Automotive, Retail, Banking, Financial Services, And Insurance (BFSI), Manufacturing, Telecommunications, Energy, Other End-User Industries

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Regional Insights –

North America was the largest region in the deep learning chipset market in 2023. Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in the deep learning chipset market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, Africa.

Key Companies –

Major companies in the market are Apple Inc., Microsoft Corporation, Samsung Electronics Co. Ltd., Huawei Technologies Co. Ltd., Amazon Web Services Inc., Intel Corporation, International Business Machines Corporation, Qualcomm Technologies Inc., Micron Technology Inc., NVIDIA Corporation, Advanced Micro Devices Inc., Texas Instruments Incorporated, MediaTek Inc., NXP Semiconductors, INSPUR Co. Ltd., Cambricon Technologies, Rockchip, Cerebras Systems Inc., Mythic, Habana Labs Ltd., BrainChip Inc.

Table of Contents
1. Executive Summary
2. Deep Learning Chipset Market Characteristics
3. Deep Learning Chipset Market Trends And Strategies
4. Deep Learning Chipset Market – Macro Economic Scenario
5. Global Deep Learning Chipset Market Size and Growth
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32. Global Deep Learning Chipset Market Competitive Benchmarking
33. Global Deep Learning Chipset Market Competitive Dashboard
34. Key Mergers And Acquisitions In The Deep Learning Chipset Market
35. Deep Learning Chipset Market Future Outlook and Potential Analysis
36. Appendix

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