(MENAFN- Straits Research) Introduction

One of the key aspects driving the growth of the neural processor market is the rising adoption of edge computing. With an increasing number of devices, such as autonomous drones, AR/VR headsets, and real-time surveillance systems, operating at the edge, there is a growing demand for low-latency, high-efficiency neural processing. Edge AI relies on processors capable of handling complex artificial intelligence tasks locally, reducing dependence on cloud infrastructure and making neural processors critical to these applications.

Moreover, the industry's heightened emphasis on energy-efficient AI hardware is accelerating the shift toward specialized chips designed for deep learning workloads. Traditional CPUs and GPUs often fall short in terms of power efficiency, leading to greater demand for Neural Processing Units (NPUs), which provide superior performance per watt.

In addition, increasing regulatory attention on data privacy and security is further fueling market expansion. On-device processing of sensitive data helps organizations comply with data protection regulations like the General Data Protection Regulation (GDPR), making neural processors increasingly valuable in both consumer electronics and enterprise environments.

Market Dynamics Increased AI workloads across industries drive the global market

A key factor propelling the growth of the global neural processor market is the surge in AI workloads across diverse industries. Sectors like automotive, healthcare, and manufacturing are increasingly integrating AI to foster productivity and drive innovation, creating a pressing need for high-speed, efficient computing. Neural processors are vital in this landscape, as they enhance the execution of deep learning and machine learning tasks.

• This trend was clearly illustrated at the GTC Paris conference in June 2025, where Nvidia revealed collaborations with European governments and enterprises to develop an industrial AI cloud. Featuring 10,000 GPUs and DGX B200 systems, this platform is intended to support companies such as BMW, Volvo, and Maserati in running advanced engineering simulations.

These practical implementations highlight how escalating AI requirements are accelerating the demand for high-performance neural processors.

Emergence of AIoT (Artificial Intelligence of Things) creates tremendous opportunities

The integration of Artificial Intelligence and the Internet of Things, collectively known as AIoT, is unlocking substantial growth opportunities for neural processor adoption across various industries. By bringing intelligence to connected devices, AIoT enables real-time data analysis, predictive insights, and autonomous decision-making directly at the edge.

• According to projections by Transforma Insights, AIoT connections are set to grow dramatically from 1.4 billion in 2023, representing just around 9% of all IoT devices featuring onboard AI, to 9.1 billion by 2033, accounting for 23%. This represents more than a sixfold increase over the next decade, with a compound annual growth rate (CAGR) surpassing 20%.

Such rapid expansion is expected to drive robust demand for energy-efficient neural processors, especially in key sectors like industrial automation, smart cities, and connected healthcare, where AIoT is revolutionizing conventional systems into intelligent, responsive networks.

Regional Analysis

North America holds a dominant position in the global neural processor market due to its strong technological infrastructure and early adoption of AI and edge computing. The U.S., in particular, is home to leading companies like NVIDIA, Intel, Qualcomm, and Apple, which are actively developing advanced NPUs for data centers, consumer electronics, and autonomous systems. For instance, Apple's Neural Engine, which is embedded in its A-series chips, supports on-device AI in millions of iPhones.

Additionally, Tesla integrates its custom Dojo neural chip for training AI models used in self-driving cars. Government support for AI innovation, such as the U.S. National AI Initiative, and growing investments in smart manufacturing and defense applications further accelerate market growth. Canada is also emerging as a prominent player, with institutions like Vector Institute leading AI research and driving demand for edge AI hardware solutions.

Key Highlights

• The global neural processor market size was valued at USD 142.35 million in 2024 and is estimated to grow from USD 169.33 million in 2025 to reach USD 678.64 million by 2033, growing at a CAGR of 18.95% during the forecast period (2025–2033).
• By component, the global neural processor market includes hardware and software.

• By technology, the market is categorized into edge AI processing, cloud-based AI processing, neuromorphic computing, and deep learning acceleration.

• By application, the market covers smartphones and wearables, autonomous vehicles, smart home devices, industrial automation, healthcare devices (e.g., diagnostic imaging), robotics, surveillance and security systems, data centers and cloud AI workloads, and others.

• By end-user industry, the segments include consumer electronics, automotive, healthcare, industrial & manufacturing, aerospace & defense, retail, telecommunications, finance, and others.
• North America is the highest shareholder in the global market.

Competitive Players

Apple Inc.

Google LLC (Alphabet Inc.)

NVIDIA Corporation

Intel Corporation

Qualcomm Technologies

Inc.

Samsung Electronics Co.

Ltd.

Huawei Technologies Co.

Ltd.

MediaTek Inc.

IBM Corporation

Advanced Micro Devices

Inc. (AMD)

ARM Ltd.

Recent Developments

• In June 2025- AMD unveiled its Ryzen AI Max+ chips at Computex, claiming superior performance over Apple's M4 Pro. These next-generation processors are designed with enhanced neural processing capabilities, catering to AI-intensive applications in PCs and edge devices. The launch underscores AMD's push into AI-powered computing, further intensifying competition in the neural processor landscape.

Segmentation

By Component:

Hardware

Neural Processing Units (NPUs)

Application-Specific Integrated Circuits (ASICs)

System-on-Chips (SoCs)

Field Programmable Gate Arrays (FPGAs)

Software

AI Development Frameworks

Neural Network Libraries

Model Optimization Tools

By Technology:

Edge AI Processing

Cloud-based AI Processing

Neuromorphic Computing

Deep Learning Acceleration

By Application:

Smartphones and Wearables

Autonomous Vehicles

Smart Home Devices

Industrial Automation

Healthcare Devices (e.g., diagnostic imaging)

Robotics

Surveillance and Security Systems

Data Centers and Cloud AI Workloads

Others

By End-User Industry:

Consumer Electronics

Automotive

Healthcare

Industrial & Manufacturing

Aerospace & Defense

Retail

Telecommunications

Finance

Others

By Regions

North America

Europe

Asia-Pacific

Latin America

The Middle East and Africa

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