Global Deep Learning Chipset Market By type (GPU, ASIC, CPU, FPGA and Others) By Technology (System-on-chip (SoC), System-in-package (SIP) and Multi-chip module and Others) By End User (Consumer Electronics, Industrial, Aerospace & Defense, Healthcare, Automotive and Others) By Region, Industry Analysis and Forecast, 2019 - 2025

Analysis of Market Size & Trends

The Global Deep Learning Chipset Market size is expected to reach $24.5 billion by 2025, rising at a market growth of 37% CAGR during the forecast period. Deep learning chips are customized Silicon chips that integrate AI technology and machine learning. Deep learning and machine learning, which are the sub-sets of Artificial Intelligence (AI) sub-sets, are used in carrying out AI related tasks. Deep learning technology has entered many industries around the world and is accomplished through applications like computer vision, speech synthesis, voice recognition, machine translation, drug discovery, game play, and robotics.

Global Deep Learning Chipset Market Size

The widespread adoption of artificial intelligence (AI) for practical business applications has brought in a range of complexities and risk factors in virtually every industry, but one thing is certain: in today's AI industry, hardware is the key to solving many of the main problems facing the sector, and chipsets are at the heart of that hardware solution. Considering AI's widespread applicability, it's almost certain that every chip will have some kind of AI system embedded in future. The engine could make a wide range of forms, from a basic AI library running on a CPU to more complex, custom hardware. The potential for AI is better fulfilled when the chipsets are designed to provide the adequate amount of computing capacity for different AI applications at the right power budget. This is a trend that leads to increased specialization and diversifying of AI-optimized chipsets.

Deep Learning Chipset Market Share

The factors influencing the development of the deep learning chipset market are increased acceptance of cloud-based technology and profound use of learning in big data analytics. A single-chip processor generates lighting effects and transforms objects each time a 3D scene is redrawn, or a graphic processing unit turns out to be very meaningful and efficient when applied to computation styles needed for neural nets. This in turn fuels the growth of the market for deep learning chipsets.

Based on type, the market is segmented into GPU, ASIC, CPU, FPGA and Others. Based on Technology, the market is segmented into System-on-chip (SoC), System-in-package (SIP) and Multi-chip module & Others. Based on End User, the market is segmented into Consumer Electronics, Industrial, Aerospace & Defense, Healthcare, Automotive and Others. Based on Regions, the market is segmented into North America, Europe, Asia Pacific, and Latin America, Middle East & Africa.

Free Valuable Insights: Global Deep Learning Chipset Market to reach a market size of $24.5 billion by 2025

Deep Learning Chipset Market Cardinal Matrix

The major strategies followed by the market participants are Product Launches. Based on the Analysis presented in the Cardinal matrix, Google, Inc., Microsoft Corporation, Samsung Electronics Co., Ltd., Intel Corporation, Amazon.com, Inc., and IBM Corporation are some of the forerunners in the Deep Learning Chipset Market. Companies such as Advanced Micro Devices, Inc., Qualcomm, Inc., Nvidia Corporation, and Xilinx, Inc. are some of the key innovators in Deep Learning Chipset Market. The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include Samsung Electronics Co., Ltd. (Samsung Group), Microsoft Corporation, Intel Corporation, Nvidia Corporation, IBM Corporation, Google, Inc., Amazon.com, Inc. (Amazon Web Services), Qualcomm, Inc., Advanced Micro Devices, Inc., and Xilinx, Inc.

Recent Strategies Deployed in Deep Learning Chipset Market

» Partnerships, Collaborations, and Agreements:

• Jan-2020: Xilinx collaborated with Telechips, a leading Automotive System on Chip (SoC) supplier. The collaboration would provide a comprehensive solution for addressing the integration of in-cabin monitoring systems (ICMS) and IVI systems.
• Dec-2019: Samsung Electronics teamed up with Baidu, a leading Chinese-language Internet search provider. Under the collaboration, the companies announced that the development of Baidu KUNLUN, its first cloud-to-edge AI accelerator has been completed. KUNLUN chip provides 512 gigabytes per second (Gbps) memory bandwidth and offers up to 260 Tera operations per second (TOPS) at 150 watts.
• Oct-2019: Microsoft announced technology collaboration with Nvidia, a technology company. The collaboration was focused on intelligent edge computing, which is designed for helping the industries in gaining and managing the insights from the data created by warehouses, retail stores, manufacturing facilities, urban infrastructure, connected buildings, and other environments.
• Oct-2019: Microsoft launched Lakefield, a dual-screen device powered by Intel’s unique processor. This device combines a hybrid CPU with Intel’s Foveros 3D packaging technology. This provides more flexibility to device makers for innovating designs, experience, and form factor.
• Jun-2019: AMD came into partnership with Samsung following which, the former company is licensing its graphics technology to Samsung for use in future mobile chips. Under this partnership, Samsung paid AMD for getting access to its RDNA graphics architecture.
• Jun-2019: Nvidia collaborated with Volvo for developing artificial intelligence that is used in self-driving trucks.
• May-2019: Samsung Electronics came into partnership with Efinix, an innovator in programmable product platforms and technologies. Under this partnership, the companies were aimed at developing Quantum eFPGAs on Samsung’s 10nm silicon process.
• Dec-2018: IBM extended its partnership with Samsung for developing 7-nanometer (nm) microprocessors for IBM Power Systems, LinuxONE, and IBM Z. The expansion was aimed at driving the performance of the unmatched system including encryption and compression speed, acceleration, memory, and I/O bandwidth, as well as system scaling.
• Jun-2018: AWS announced its collaboration with Cadence Design Systems. The collaboration was aimed at delivering a Cadence Cloud portfolio to electronic systems and semiconductor design.
• Mar-2018: Nvidia came into partnership with Arm for bringing deep learning interface to billions of consumer electronics, mobile, and Internet of Things devices.

» Acquisition and Mergers:

• Aug-2019: Xilinx took over Solarflare, a provider of high-performance, low latency networking solutions. The acquisition helped in generating more revenues and enabled new marketing and R&D funds for the future.
• Apr-2019: Intel completed the acquisition of Omnitek, a provider of video and vision field-programmable gate array (FPGA). Through the acquisition, the FPGA processor business of the company has been doubled.
• Jul-2018: Intel took over eASIC, a fabless semiconductor company. The acquisition bolstered the company's business in providing chips.
• Apr-2017: AMD acquired Nitero, a company engaged in providing technology to connect VR headsets wirelessly to PCs. The acquisition helped the company in getting control over VR experiences.

» Product Launches and Product Expansions:

• Dec-2019: Nvidia launched Drive AGX Orin, a new Orin AI processor or system-on-chip (SoC). This processor improves power efficiency and performance. This processor is used in evolving the automotive business.
• Dec-2019: AWS unveiled Graviton2, the next-generation of its ARM processors. It is a custom chip that is designed with 7nm architecture and based on 64-bit ARM Neoverse cores.
• Nov-2019: AMD launched two new Threadripper 3 CPUs with 24 and 32 cores. Both these CPUs will be integrated into AMD’s new TRX40 platform using the new sTRX4 socket.
• Nov-2019: Intel unveiled Ponte Vecchio GPUs, a graphics processing unit (GPU) architecture. This chip was designed for handling the artificial intelligence loads and heavy data in the data center.
• Nov-2019: Intel launched Stratix 10 GX 10M, a new FPGA. This consists of two large FPGA dies and four transceiver tiles and has a total of 10.2 million logic elements and 2304 user I/O pins.
• Oct-2018: Google launched TensorFlow, the popular open-source artificial intelligence framework. This framework runs deep learning, machine learning, and other predictive and statistical analytics workloads. This simplifies training models, the process of acquiring data, refining future results, and serving predictions.
• Sep-2019: AWS released Amazon EC2 G4 GPU-powered Amazon Elastic Compute Cloud (Amazon EC2) instances. It delivers up to 1.8 TB of local NVMe storage and up to 100 Gbps of networking throughput to AWS custom Intel Cascade Lake CPUs and NVIDIA T4 GPUs.
• Aug-2019: Xilinx released Virtex UltraScale+ VU19P, a 16nm device with 35 billion transistors. It has four chips on an interposer. It is the world's largest field-programmable gate array (FPGA) and has 9 million logic cells.
• May-2019: Nvidia introduced NVIDIA EGX, an accelerated computing platform. This platform was aimed at allowing the companies in performing low-latency AI at the edge for perceiving, understanding, and acting in real-time on continuous streaming data between warehouses, factories, 5G base stations, and retail stores.
• Nov-2018: AWS introduced Inferentia and Elastic Inference, two chips and 13 machine learning capabilities and services. Through these launches, the company aimed towards attracting more developers.
• Sep-2018: Qualcomm unveiled Snapdragon Wear 3100 chipset. This chipset is used in smartwatches and has extended battery life.
• Aug-2018: AMD introduced B450 chipset for Ryzen processors. The chip runs about 2 watts lower in power than B350 chipset.
• Jul-2018: Google introduced Tensor Processing Units or TPUs, the specialized chips. This chip lives in data centers of the company and simplifies the AI tasks. These chips are used in enterprise jobs.
• Apr-2018: Qualcomm launched QCS605 and QCS603 SoCs, two new system-on-chips. These chips combine image signal processor, CPU, AI, GPU technology for accommodating several camera applications, smart displays, and robotics.

Scope of the Deep Learning Chipset Market Analysis

Market Segmentation:

By Compute Capacity

• High
• Low

By Type

• GPU
• ASIC
• CPU
• FPGA
• Others

By Technology

• System-on-chip (SoC)
• System-in-package (SIP)
• Multi-chip module & Others

By End User

• Consumer Electronics
• Industrial
• Aerospace & Defense
• Healthcare
• Automotive
• Others

By Geography

• North America
  • US
  • Canada
  • Mexico
  • Rest of North America
• Europe
  • Germany
  • UK
  • France
  • Russia
  • Spain
  • Italy
  • Rest of Europe
• Asia Pacific
  • China
  • Japan
  • India
  • South Korea
  • Singapore
  • Malaysia
  • Rest of Asia Pacific
• LAMEA
  • Brazil
  • Argentina
  • UAE
  • Saudi Arabia
  • South Africa
  • Nigeria
  • Rest of LAMEA

Companies Profiled

• Samsung Electronics Co., Ltd. (Samsung Group)
• Microsoft Corporation
• Intel Corporation
• Nvidia Corporation
• IBM Corporation
• Google, Inc.
• com, Inc. (Amazon Web Services)
• Qualcomm, Inc.
• Advanced Micro Devices, Inc.
• Xilinx, Inc.