Neural Processing Unit Market Size, Share, Opportunities, And Trends By Type (Multilayer Perceptron, Convolutional Neural Network, Recursive Neural Network, Recurrent Neural Network, Sequence-to-sequence Model, Shallow Neural Network, Long Short-term Memory), By Component (Hardware, Software, Services), By Application (Automotive, Electronic, Defense, Aerospace, Entertainment, Others), And By Geography - Forecasts From 2025 To 2030

The neural processing unit market is projected to grow at a CAGR of 16.26% from US$11,496.383 Million in 2025 to US$24,420.685 Million in 2030. 

A neural processor, sometimes referred to as a neural processing unit (NPU), is a specialized circuit that carries out all the arithmetic and control logic needed to execute machine learning algorithms. These algorithms typically work with predictive models such as random forests (RFs) or artificial neural networks (ANNs).  Digital and analog methods can be used to build neural processors.  In analog design, only a small number of transistors are needed to replicate the differential equations of neurons.  These units, therefore, theoretically consume less energy than digital neuromorphic computers. 

Neural Processing Unit Market Overview & Scope

The neural processing unit market is segmented by:

• Type: The neural processing unit market is divided into several segments based on type, including Sequence-to-sequence Model, Shallow Neural Network, Convolutional Neural Network, Recursive Neural Network, Recurrent Neural Network, Multilayer Perceptron, and Long Short-term Memory.  Neural processors of various kinds are regularly used in a wide range of industries, including manufacturing, entertainment, sports, automotive, aerospace, and electronics.  Apart from this, deep learning commonly uses convolutional neural networks to process data, understand images, and lessen the need for human intervention, all of which contribute to the segment's growth in the market.
• Component: The neural processor market, based on components, has been divided into three segments: hardware, software, and services. The hardware category is expected to dominate the global neural processor market because of the rapidly evolving innovations and developments in the hardware used in neural processing.
• Application: The global neural processors market has been divided into several segments based on their respective applications, including automotive, electronics, defense, aerospace, entertainment, and others. Over the projected time, it is expected that the application of neural networks in the automotive industry will grow rapidly. Neural processor use in the industry is increasing due to the need for automation features like voice commands, automated driving, and artificial intelligence in automobiles.
• Region:  The market is segmented into five major geographic regions, namely North America, South America, Europe, the Middle East, Africa, and Asia-Pacific. North America is anticipated to dominate the market and might grow at the fastest CAGR.

Top Trends Shaping the Neural Processing Unit Market

1. Growing Need for Applications of AI and Machine Learning

• The growing need for machine learning and artificial intelligence (AI) applications in a variety of industries is driving the neural processing unit market.  The need for strong processing capabilities has been greater as companies want to use AI to boost consumer experiences, increase operational efficiencies, and spur innovation.  While running large-scale machine learning algorithms, neural processors are made to handle complicated data sets and carry out calculations that conventional CPUs and GPUs find difficult.  The growth of AI applications in fields like natural language processing, predictive analytics, healthcare diagnostics, and driverless cars is driving this demand.

2. Technological Progress in Neural Networks

• Neural network technology is advancing quickly, which is a key factor propelling market expansion.  The capabilities of neural processors are being increased by advancements in neural network topologies and deep learning techniques.  Organizations are progressively implementing complex neural network models that require more potent processing technology to enhance the performance of their AI applications. These developments are making it possible to create neural processors that can effectively handle greater model sizes and more intricate neural networks to achieve high accuracy and performance in AI applications.

Neural Processing Unit Market Growth Drivers vs. Challenges

Drivers:

• Increasing Use of Edge Computing: The market expansion for neural processors is being significantly impacted by the growing use of edge computing. Businesses are searching for effective methods to handle and analyze data at the point of generation due to the proliferation of IoT devices and the requirement for real-time data processing. In this context, neural processors are essential since they give edge devices strong computational capabilities. Edge computing is increasing demand for specialized hardware that can effectively manage AI workloads on-site by lowering latency, lowering bandwidth needs, and protecting data privacy.
• Increasing funding for cutting-edge technology: Every organization can benefit greatly from investing in AI and neural networks.  Object and sound detection, sound recognition, object tracking, facial recognition, keyword spotting, and packet inspection are all powered by neural network processors.  The government has also made significant investments in the development of AI and real-time analytics, which are expected to propel the processor industry.

Challenges:

• The availability of neural processor (NPU) compatible software and tools: Neural processor (NPU) usage across a range of applications is significantly influenced by the availability of suitable software and tools. The absence of customized software that can fully utilize the capabilities of neural processing units (NPUs) presents a difficulty for many developers and organizations, despite the rapid improvements in neural processing technology. This restriction may make it more difficult to apply NPUs effectively in practical settings, which could impede innovation and adoption rates.

Neural Processing Unit Market Regional Analysis

• North America: The market for neural processors in North America is expanding rapidly, driven by the rising need for complex AI applications. Neural processor usage is accelerating in several industries, including healthcare, banking, and the automotive sector, due to the growth of cloud computing and edge devices. Furthermore, significant regional firms are making significant R&D investments to improve neural processor performance and capabilities.

Neural Processing Unit Market Competitive Landscape

The market is moderately fragmented, with many key players including NVIDIA Corporation, Intel Corporation, Qualcomm Incorporated, and AMD.

• Product Innovation: In September 2024, the Core Ultra 200V processors from Intel Corporation improved laptop power economy and computational capacity by introducing a neural processing unit that is four times quicker than the previous version.
• Sustainable product launch: In June 2024, at the Computex technology trade expo, Advanced Micro Devices Inc. showcased new neural processing units made for on-device AI workloads in AI PCs through the introduction of its AI processors, which included the MI325X accelerator. It is anticipated that the MI350 series will have 35 times greater inference capabilities than previous models, demonstrating AMD's dedication to major performance enhancements.

Neural Processing Unit Market Segmentation:  

By Type

• Multilayer Perceptron
• Convolutional Neural Network
• Recursive Neural Network
• Recurrent Neural Network
• Sequence-to-sequence Model
• Shallow Neural Network
• Long Short-term Memory

By Component

• Hardware
• Software
• Services

By Application

• Automotive
• Electronic
• Defense
• Aerospace
• Entertainment
• Others

By Region

• North America
  • USA
  • Mexico
  • Others
• South America
  • Brazil
  • Argentina
  • Others
• Europe
  • United Kingdom
  • Germany
  • France
  • Spain
  • Others
• Middle East & Africa
  • Saudi Arabia
  • UAE
  • Others
• Asia Pacific
  • China
  • Japan
  • India
  • South Korea
  • Taiwan
  • Others

1. EXECUTIVE SUMMARY 

2. MARKET SNAPSHOT

2.1. Market Overview

2.2. Market Definition

2.3. Scope of the Study

2.4. Market Segmentation

3. BUSINESS LANDSCAPE 

3.1. Market Drivers

3.2. Market Restraints

3.3. Market Opportunities 

3.4. Porter’s Five Forces Analysis

3.5. Industry Value Chain Analysis

3.6. Policies and Regulations 

3.7. Strategic Recommendations 

4. TECHNOLOGICAL OUTLOOK

5. NEURAL PROCESSING UNIT MARKET BY TYPE 

5.1. Introduction

5.2. Multilayer Perceptron

5.3. Convolutional Neural Network

5.4. Recursive Neural Network

5.5. Recurrent Neural Network

5.6. Sequence-to-sequence Model

5.7. Shallow Neural Network

5.8. Long Short-term Memory

6. NEURAL PROCESSING UNIT MARKET BY COMPONENT

6.1. Introduction

6.2. Hardware

6.3. Software

6.4. Services

7. NEURAL PROCESSING UNIT MARKET BY APPLICATION

7.1. Introduction

7.2. Automotive

7.3. Electronic

7.4. Defense

7.5. Aerospace

7.6. Entertainment

7.7. Others

8. NEURAL PROCESSING UNIT MARKET BY GEOGRAPHY 

8.1. Introduction

8.2. North America

8.2.1. By Type

8.2.2. By Component

8.2.3. By Application

8.2.4. By Country

8.2.4.1. USA

8.2.4.2. Canada

8.2.4.3. Mexico

8.3. South America

8.3.1. By Type

8.3.2. By Component

8.3.3. By Application

8.3.4. By Country

8.3.4.1. Brazil

8.3.4.2. Argentina

8.3.4.3. Others

8.4. Europe

8.4.1. By Type

8.4.2. By Component

8.4.3. By Application

8.4.4. By Country

8.4.4.1. United Kingdom

8.4.4.2. Germany

8.4.4.3. France

8.4.4.4. Spain

8.4.4.5. Others

8.5. Middle East and Africa

8.5.1. By Type

8.5.2. By Component

8.5.3. By Application

8.5.4. By Country

8.5.4.1. Saudi Arabia

8.5.4.2. UAE

8.5.4.3. Others

8.6. Asia Pacific

8.6.1. By Type

8.6.2. By Component

8.6.3. By Application

8.6.4. By Country

8.6.4.1. China

8.6.4.2. Japan

8.6.4.3. India

8.6.4.4. South Korea

8.6.4.5. Taiwan

8.6.4.6. Others

9. COMPETITIVE ENVIRONMENT AND ANALYSIS

9.1. Major Players and Strategy Analysis

9.2. Market Share Analysis

9.3. Mergers, Acquisitions, Agreements, and Collaborations

9.4. Competitive Dashboard

10. COMPANY PROFILES

10.1. NVIDIA 

10.2. Intel 

10.3. Qualcomm 

10.4. Google 

10.5. Apple 

10.6. Samsung 

10.7. Huawei

10.8. Arm 

11. APPENDIX

11.1. Currency 

11.2. Assumptions

11.3. Base and Forecast Years Timeline

11.4. Key benefits for the stakeholders

11.5. Research Methodology 

11.6. Abbreviations 

NVIDIA

Intel

Qualcomm

Google

Apple

Samsung

Huawei

Arm