Neural Processors Market Forecast 2025-2035: Global Value to Reach USD 1,010 Million by 2035 at 19.1% CAGR ' | Future Market Insights'

NEWARK, Del., Feb. 12, 2026 /PRNewswire/ -- Future Market Insights (FMI) projects the Neural Processors Market to grow from USD 176 million in 2025 to USD 1,010 million by 2035, advancing at a 19.1% CAGR. This surge is being driven by the rising integration of dedicated AI hardware in smartphones, wearables, tablets, smart TVs, autonomous vehicles, and edge computing devices. Neural processing units (NPUs) are increasingly being used to accelerate machine learning inference tasks on-device—reducing latency, boosting power efficiency, and enhancing privacy by minimizing cloud dependency.

Industry Snapshot: Neural Processors Demand (2025–2035)

• Neural processors market size (2025): USD 176 million
• Neural processors market size (2035): USD 1,010 million
• Forecast CAGR (2025–2035): 19.1%
• Leading operation segment (2025): Inference operations (67% share)
• Leading application segment (2025): Smartphones and tablets (24.4% share)
• Key growth region: America (United States)
• Key players (named): NVIDIA Corporation; Intel Corporation; Google (TPU); IBM Corporation; Qualcomm Inc.
• What NPUs are used for: On-device inference acceleration to reduce latency, improve power efficiency, and support privacy by minimizing cloud dependency

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Market Momentum: A Fast Market Built on Faster Inference—and Less Cloud

The Neural Processors Market is scaling with the shift from cloud-dependent AI to edge AI and device-level intelligence. Growth is being reinforced by a practical performance logic: inference tasks such as facial recognition, speech-to-text, and noise suppression are increasingly executed on-device to cut response time and reduce data movement. As dedicated AI hardware becomes more common across consumer electronics and edge computing devices, NPUs are moving from a "feature" component to a foundational part of modern AI-enabled product design.

A forward-looking industry signal is captured in a March 2025 interview with DIGITIMES, where Amir Panush, CEO of Ceva, highlighted the company's strategic focus on edge AI, stating: "Edge AI presents significant industry opportunities and will be a key growth engine for Ceva in the coming years." This statement reinforces the industry's emphasis on real-time edge applications and the expanding role of neural processor technologies.

Why the Market is Growing

Growth in the Neural Processors Market is being powered by:

• Rising integration of dedicated AI hardware in smartphones, wearables, tablets, smart TVs, autonomous vehicles, and edge computing devices
• Increasing need for on-device artificial intelligence (AI) across smartphones, automotive, healthcare, and consumer electronics
• Edge computing replacing cloud-based AI for faster real-time processing and enhanced privacy
• NPUs supporting AI features such as face unlock, voice recognition, predictive maintenance, and autonomous navigation
• 5G and IoT ecosystem maturity, positioning neural processors as key enablers of low-latency, cloud-independent AI

At the same time, the industry is contending with high development costs, architectural fragmentation, and geopolitical tensions that are limiting scalability—particularly for new entrants and general-purpose device adoption.

Segment Spotlight

1) Operation: Inference Operations Lead (67% Share in 2025)

Inference operations are expected to account for 67% of the neural processor industry in 2025, driven by the rising need for efficient AI model deployment at the edge. These tasks—including facial recognition, speech-to-text, and noise suppression—are increasingly executed on-device to enhance privacy and reduce latency, removing dependency on cloud infrastructure.

Chipmakers including NVIDIA, Qualcomm, and Intel are focusing on designing NPUs specialized for low-power inference applications across consumer and industrial sectors. Arm's Ethos-U65 microNPU is cited as delivering sub-milliwatt AI performance for IoT endpoints, supporting edge deployment in smart cities and wearables. Cloud interoperability is also noted as enabling local inference while syncing with centralized models, reinforcing inference as a core design priority.

2) Application: Smartphones and Tablets Lead (24.4% Share in 2025)

Smartphones and tablets are projected to capture 24.4% of the industry in 2025, as NPUs are increasingly integrated to power AI-driven features such as real-time language processing, intelligent photography, and device customization.

An example cited is Google's Tensor G3 chip introduced in 2024, which brought a triple-core TPU to Pixel smartphones to enhance language processing and generative image editing. Major players including Apple, Samsung, and Xiaomi are also referenced as continuing to improve NPU performance with each hardware cycle. According to Counterpoint Research, 89% of smartphones released in Q1 2025 are expected to include dedicated AI hardware, supporting the increasing reliance on NPUs in mobile platforms. Tablets are also positioned as benefiting from NPUs for enhanced UI responsiveness and offline AI capabilities.

Drivers, Opportunities, Trends, Challenges

Drivers: Demand is increasing due to the growing need for on-device AI across industries and the broader shift toward edge computing that supports real-time processing and privacy. NPUs are integrated into chipsets to enable AI features including face unlock, voice recognition, predictive maintenance, and autonomous navigation—supporting low-latency AI applications as 5G and IoT ecosystems mature.

Opportunities: The market's growth pathway is strengthened by expanding use cases across smartphones, automotive, healthcare, and consumer electronics. Cloud interoperability is highlighted as enabling local inference while syncing with centralized models—supporting hybrid deployment strategies that maintain performance while keeping latency low.

Trends: Key market direction centers on inference-first hardware strategies, edge deployment, and increasing integration of dedicated AI hardware across consumer and edge devices. The industry is also described as shifting away from cloud-only AI toward on-device intelligence.

Challenges: Industry scalability is constrained by high development costs and architectural fragmentation. Designing NPUs requires specialized knowledge in chip architecture and deep learning, concentrated in leading firms, while startups face high costs and lack of standardized software frameworks. Fragmentation across use cases (training vs inference) creates compatibility issues. Geopolitical tensions—including USA export restrictions on advanced chips—are also limiting access to advanced AI hardware in key regions.

Country Growth Outlook (CAGR) | 2025–2035

Country-level divergence is described as reflecting strong AI and machine learning investment in the United States, rapid adoption in China, and increasing focus on AI research and development in India, South Korea, and the UK. The United States is identified as the key growth region (America).

Competitive Landscape

The global Neural Processors Market features dominant, key, and emerging players. Dominant players cited include NVIDIA Corporation, Intel Corporation, and Google LLC, supported by product portfolios, R&D capabilities, and distribution networks across AI, machine learning, and data center sectors.

Key players including IBM Corporation, Qualcomm Inc., and CEVA Inc. are cited as offering specialized solutions. Emerging players cited include BrainChip Holdings Ltd., Graphcore Limited, and Teradeep Inc., focusing on innovation and cost-effective solutions.

Key Players in Neural Processors Market (listed):
Intel Corporation; IBM Corporation; Google (TPU); Qualcomm Inc.; CEVA Inc.; NVIDIA Corporation; Advanced Micro Devices (AMD); BrainChip Holdings Ltd.; Graphcore Limited; Teradeep Inc.

For an in-depth analysis of evolving formulation trends and to access the complete strategic outlook through 2036, visit the official report page at: https://www.futuremarketinsights.com/reports/neural-processors-market 

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