NEW
YORK, July 12, 2024 /PRNewswire/ -- The global
deep learning chips market size is estimated to grow by
USD 42.39 billion from 2024-2028,
according to Technavio. The market is estimated to grow at a CAGR
of 50.22% during the forecast period. Rise in adoption of deep
learning chips in autonomous vehicles is driving market growth,
with a trend towards advances in quantum computing. However, dearth
of technically skilled workers for deep learning chip development
poses a challenge. Key market players include Achronix
Semiconductor Corp., Advanced Micro Devices Inc., Alphabet Inc.,
Amazon.com Inc., Cerebras, China Cambrian Technology Co. Ltd., Flex
Logix Technologies Inc., Fujitsu Ltd., Graphcore Ltd., Groq Inc.,
Intel Corp., International Business Machines Corp., MediaTek Inc.,
NVIDIA Corp., Qualcomm Inc., Samsung Electronics Co. Ltd., Synopsys
Inc., Syntiant Corp., Taiwan Semiconductor Manufacturing Co. Ltd.,
and ThinkForce.
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Forecast
period
|
2024-2028
|
Base Year
|
2023
|
Historic
Data
|
2018 - 2022
|
Segment
Covered
|
Technology
(System-on-Chip, System-in-Package, Multi-chip Module, and Others),
End-user (BFSI, IT and telecom, Media and advertising, and Others),
and Geography (North America, Europe, APAC, South America, and
Middle East and Africa)
|
Region
Covered
|
North America, Europe,
APAC, South America, and Middle East and Africa
|
Key companies
profiled
|
Achronix Semiconductor
Corp., Advanced Micro Devices Inc., Alphabet Inc., Amazon.com Inc.,
Cerebras, China Cambrian Technology Co. Ltd., Flex Logix
Technologies Inc., Fujitsu Ltd., Graphcore Ltd., Groq Inc., Intel
Corp., International Business Machines Corp., MediaTek Inc., NVIDIA
Corp., Qualcomm Inc., Samsung Electronics Co. Ltd., Synopsys Inc.,
Syntiant Corp., Taiwan Semiconductor Manufacturing Co. Ltd., and
ThinkForce
|
Key Market Trends Fueling Growth
The rapid adoption of smart devices and increasing automation
are driving the generation of vast amounts of unstructured data.
Processing this data demands hardware with advanced computational
abilities. While conventional computers have seen significant
processing speed improvements, they are still insufficient for
handling the data deluge. Consequently, market players worldwide
are investing heavily in quantum computing technology. Quantum
computing uses quantum mechanics principles to achieve higher
computational power, with qubits being the primary difference from
conventional bits. Qubits exist in multiple states at once,
enabling quantum computers to perform simultaneous calculations, a
concept known as quantum parallelism. This technology is gaining
traction in AI, ML, and Big Data due to its speed advantages.
However, building powerful quantum computers necessitates the
development of efficient deep learning chips. The focus on creating
algorithms and procedures for quantum computing will lead to the
development of these chips, fueling the growth of the deep learning
chip market during the forecast period.
The Deep Learning Chips Market is experiencing significant
growth due to the increasing demand for advanced technologies like
voice recognition, speech synthesis, machine translation, game
playing, drug discovery, robotics, and more. Silicon chips are at
the heart of this trend, with technology companies investing
heavily in AI hardware such as neural network processors, machine
learning chips, artificial intelligence accelerators, and deep
learning accelerators. Chip types include GPU chips, CPU chips,
ASIC chips, FPGA chips, and hardware accelerators.
Edge computing chips and cloud computing chips are also in high
demand for delivering real-time processing and data center
solutions. Industry verticals like media & advertising, IT
& telecom, healthcare, automotive & transportation, working
from home, supply chain, stock market, and business confidence are
major consumers of these chips. The skilled workforce is essential
for bug fixing and cloud implementation, while manufacturing units
ensure timely delivery. Quantum computing chips are a future trend,
while neuromorphic computing chips are gaining traction for their
ability to mimic the human brain.
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Market Challenges
- The deep learning chip market is experiencing rapid growth due
to increasing demand for advanced technologies in various
industries such as healthcare, transportation, finance, and
security. However, the development of deep learning chips faces a
significant challenge: a shortage of skilled workers. This shortage
can be attributed to the newness of the field, requiring
specialized technical expertise in areas like electrical
engineering, computer science, machine learning, data analysis, and
proficiency in programming languages such as Python and C++. Few
universities and technical schools offer specialized programs in
deep learning chip development, resulting in a limited pool of
graduates with the necessary skills. The high demand for deep
learning chips intensifies competition among companies for skilled
workers, driving up salaries, benefits, and incentives. Smaller
companies may struggle to compete, potentially hindering the growth
of the global deep learning chip market.
- The Deep Learning Chips Market is experiencing significant
growth due to the increasing demand for AI hardware solutions.
Neural network processors, machine learning chips, and artificial
intelligence accelerators are driving this market forward. However,
challenges exist in the form of GPU and CPU chips, which while
effective, are not optimized for deep learning. ASIC,
FPGA, and hardware accelerators offer alternatives, but come with
their own complexities. Edge computing and cloud computing chips,
data center chips, and neuromorphic computing chips are also in
play. Innovations in AI chip architectures, design, manufacturing,
testing, validation, integration, optimization, performance,
scalability, efficiency, reliability, and security are key. AI chip
vendors and startups are pushing boundaries with new applications,
technologies, and solutions in machine learning, computer vision,
and more. The goal is to create efficient, reliable, and secure AI
chips for various industries and applications.
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challenges - Download a Sample Report
Segment Overview
This deep learning chips market report extensively covers market
segmentation by
- Technology
- 1.1 System-on-Chip
- 1.2 System-in-Package
- 1.3 Multi-chip Module
- 1.4 Others
- End-user
- 2.1 BFSI
- 2.2 IT and telecom
- 2.3 Media and advertising
- 2.4 Others
- Geography
- 3.1 North America
- 3.2 Europe
- 3.3 APAC
- 3.4 South America
- 3.5 Middle East and
Africa
1.1 System-on-Chip- System-on-Chips (SoCs) are gaining
popularity due to their versatility, power, and efficiency in
handling complex computational tasks, particularly in deep learning
applications. SoCs are highly integrated microchips that combine
all necessary components, such as processing and communications,
onto a single piece of silicon. This integration is beneficial for
deep learning as it integrates CPUs, GPUs, and necessary memory on
a single chip, enhancing performance and energy efficiency. The
SoC's architecture can be customized and optimized to meet specific
device or application needs, making it a flexible and adaptable
technology. Its real-time processing capabilities are essential for
deep learning applications, such as autonomous vehicles,
healthcare, retail, and manufacturing, which require handling
massive amounts of data and executing complex algorithms. SoCs
enable real-time processing, necessary for efficient
decision-making in autonomous vehicles, and transfer of large
datasets, improving deep learning performance. The SoC segment's
growth in the global deep learning chips market is expected to
increase due to these advantages, making it an attractive option
for device manufacturers.
For more information on market segmentation with geographical
analysis including forecast (2024-2028) and historic data (2018 -
2022) - Download a Sample Report
Research Analysis
The Deep Learning Chips Market refers to the industry dedicated
to producing specialized hardware for accelerating artificial
intelligence (AI) workloads, including neural network processors,
machine learning chips, artificial intelligence accelerators, deep
learning accelerators, and various types of chips optimized for AI
inference and training. These chips come in various forms such as
GPU chips, CPU chips, ASIC chips, FPGA chips,
high-performance computing chips, embedded AI chips, low-power AI
chips, and neural processing units. AI chip integration and
optimization are crucial for enhancing performance, scalability,
efficiency, reliability, and security. AI chip applications span
across industries, including healthcare, finance, automotive,
gaming, and consumer electronics. Continuous innovations in AI chip
technologies are driving the market's growth, enabling faster and
more accurate AI processing.
Market Research Overview
The Deep Learning Chips Market encompasses a range of hardware
solutions designed to accelerate artificial intelligence (AI) and
machine learning (ML) workloads. These chips include neural network
processors, machine learning chips, artificial intelligence
accelerators, deep learning accelerators, GPU chips, CPU chips,
ASIC chips, FPGA chips, hardware accelerators, edge
computing chips, cloud computing chips, data center chips,
neuromorphic computing chips, quantum computing chips, parallel
processing chips, high-performance computing chips, embedded AI
chips, low-power AI chips, AI inference chips, AI training chips,
on-device AI chips, neural processing units, AI co-processors, and
various AI chip architectures. The market also involves AI chip
design, manufacturing, testing, validation, integration,
optimization, performance, scalability, efficiency, reliability,
security, and various chip types such as System-on-chip (SoC),
System-in-package (SiP), and Multi-chip module (MCM). Applications
of these chips span across various industry verticals including
media & advertising, IT & telecom, healthcare, automotive
& transportation, working from home, supply chain, stock
market, business confidence, and manufacturing units. The market is
driven by the increasing demand for AI and ML in computer vision,
voice recognition, speech synthesis, machine translation, game
playing, drug discovery, robotics, and other fields. The market is
also influenced by technological advancements, skilled workforce,
bug fixing, cloud implementation, delivery, and various chip
technologies and innovations.
Table of Contents:
1 Executive Summary
2 Market Landscape
3 Market Sizing
4 Historic Market Size
5 Five Forces Analysis
6 Market Segmentation
- Technology
-
- System-on-Chip
- System-in-Package
- Multi-chip Module
- Others
- End-user
-
- BFSI
- IT And Telecom
- Media And Advertising
- Others
- Geography
-
- North America
- Europe
- APAC
- South America
- Middle East And Africa
7 Customer Landscape
8 Geographic Landscape
9 Drivers, Challenges, and Trends
10 Company Landscape
11 Company Analysis
12 Appendix
About Technavio
Technavio is a leading global technology research and advisory
company. Their research and analysis focuses on emerging market
trends and provides actionable insights to help businesses identify
market opportunities and develop effective strategies to optimize
their market positions.
With over 500 specialized analysts, Technavio's report library
consists of more than 17,000 reports and counting, covering 800
technologies, spanning across 50 countries. Their client base
consists of enterprises of all sizes, including more than 100
Fortune 500 companies. This growing client base relies on
Technavio's comprehensive coverage, extensive research, and
actionable market insights to identify opportunities in existing
and potential markets and assess their competitive positions within
changing market scenarios.
Contacts
Technavio Research
Jesse Maida
Media & Marketing Executive
US: +1 844 364 1100
UK: +44 203 893 3200
Email: media@technavio.com
Website: www.technavio.com/
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SOURCE Technavio