Global Compute-in-Memory Market Size, Share and Forecasts 2031

Key Findings

• Compute-in-memory (CIM) technology embeds computation directly within memory arrays, reducing data transfer bottlenecks between processing and storage units.

• The approach addresses the “memory wall” challenge in von Neumann architectures, significantly improving performance and power efficiency.

• Adoption is strongest in AI acceleration, edge computing, and high-performance workloads where data movement dominates energy costs.

• Emerging CIM architectures leverage SRAM, DRAM, and non-volatile memories like ReRAM, MRAM, and PCM for analog and digital in-memory computing.

• The technology is vital for accelerating neural networks, recommendation engines, and signal processing in compact and low-power environments.

• Governments and industry consortia are funding research programs to achieve scalability, reliability, and manufacturability at advanced process nodes.

• Startups and universities are driving early breakthroughs, while semiconductor giants are integrating CIM into heterogeneous SoCs and AI accelerators.

• Asia-Pacific leads in R&D and pilot fabs, while North America drives ecosystem partnerships for AI applications.

• Challenges include analog noise, limited precision, endurance issues, and integration with existing toolchains.

• Compute-in-memory is transitioning from research labs to early commercialization in AI inference chips and edge devices.

Compute-in-Memory Market Size and Forecast

The global compute-in-memory market was valued at USD 820 million in 2024 and is projected to reach USD 5.1 billion by 2031, growing at a CAGR of 29.3%. Growth is driven by AI workloads, the demand for edge inference acceleration, and energy-efficient computing architectures. Expansion is also fueled by collaborations between memory vendors, chipmakers, and research labs. Pilot deployments in AI edge devices, IoT nodes, and datacenter inference units are proving CIM’s commercial viability.

Market Overview

Compute-in-memory represents a paradigm shift in computing, collapsing the boundary between logic and memory to reduce latency and power inefficiencies. Traditional von Neumann architectures suffer from bottlenecks in data shuttling, consuming disproportionate energy. By performing arithmetic and logic directly in the memory array, CIM reduces this overhead dramatically. Both digital and analog CIM implementations are being pursued, with analog CIM promising high parallelism and energy efficiency, while digital CIM offers better accuracy and integration with existing EDA flows. Industries ranging from AI, defense, autonomous systems, to big data analytics are exploring CIM for specialized acceleration needs.

Future Outlook

The next decade will see compute-in-memory evolve from niche adoption toward mainstream semiconductor integration. Early success in AI inference accelerators will expand into broader applications including autonomous driving, 5G/6G communication, and edge analytics. Continued advancements in resistive and magnetic memory technologies will underpin scalability, while hybrid CIM-digital architectures will mitigate precision issues. Partnerships between chip manufacturers, foundries, and cloud providers will be essential for ecosystem maturity. By 2031, CIM is expected to become a key enabler for ultra-low-power edge AI and exascale data processing.

Compute-in-Memory Market Trends

• Emergence of CIM for AI Acceleration
  Compute-in-memory is increasingly used for neural network inference, enabling energy-efficient acceleration of matrix-vector multiplications. AI inference requires massive parallel operations, which CIM can perform directly in memory with minimal data movement. This not only reduces energy costs but also allows edge devices to run models locally without reliance on cloud compute. Startups and major chipmakers are embedding CIM modules in AI SoCs, fueling a wave of innovation in embedded intelligence.

• Adoption of Non-Volatile Memories in CIM Architectures
  Technologies such as ReRAM, MRAM, and PCM are gaining traction as enablers of analog and digital CIM. These memories offer higher density, persistent storage, and compatibility with parallel computation schemes. Non-volatile CIM architectures reduce standby power consumption, making them ideal for IoT, wearables, and sensor-edge analytics. Vendors are racing to demonstrate endurance and reliability at scale to make NVM-CIM a viable option for mass production.

• Integration into Heterogeneous SoCs and Accelerators
  CIM is increasingly seen as a building block in heterogeneous architectures where CPUs, GPUs, and accelerators work together. Compute-in-memory modules can offload specific tasks such as vector multiplications or convolutions, enabling overall system efficiency. Major semiconductor companies are embedding CIM into custom AI accelerators designed for datacenter inference and mobile SoCs. This trend is accelerating the adoption of CIM into mainstream product roadmaps.

• Analog CIM for Ultra-Low-Power Edge Applications
  Analog CIM, despite its precision challenges, is showing strong potential for ultra-low-power computing at the edge. Applications in wearables, drones, and IoT sensors require low latency and energy efficiency, making analog CIM attractive. Research groups and startups are focusing on mitigating noise and variability through calibration and redundancy techniques. With growing demand for distributed AI, analog CIM solutions are expected to carve out significant market share.

• Expansion of Global Research Collaborations
  Research consortia involving academia, government labs, and private firms are fueling CIM innovation. Programs in the U.S., China, South Korea, and Europe are funding pilot fabs and prototype designs. This collaborative approach ensures faster scaling from concept to manufacturable solutions. Shared IP frameworks and open benchmarks are also accelerating CIM adoption by easing entry barriers for startups and mid-tier chipmakers.

Market Growth Drivers

• Explosion of AI Workloads
  AI models are increasingly data-intensive, requiring efficient hardware for inference and training. Compute-in-memory offers significant acceleration by cutting down on redundant data transfers. The technology aligns perfectly with the growing demand for energy-efficient AI, particularly in mobile and edge contexts. This alignment is positioning CIM as a central pillar in the AI hardware roadmap.

• Need for Energy-Efficient Computing at the Edge
  Edge devices like drones, smart cameras, and industrial IoT nodes require local intelligence without relying on cloud infrastructure. CIM enables low-power inference directly on-device, enhancing responsiveness and reducing connectivity costs. As 5G and IoT ecosystems expand, demand for such low-power computing accelerators will rise significantly. Energy savings are not just a feature but a necessity, making CIM a highly sought-after solution.

• Advancements in Emerging Memory Technologies
  The maturation of ReRAM, MRAM, and PCM technologies is driving compute-in-memory adoption. These memories provide non-volatility, high endurance, and compatibility with parallel in-memory computation. Continuous innovation in materials science and device physics is improving the scalability and manufacturability of CIM. This progress provides a strong technological foundation for long-term growth.

• Government and Defense Investments
  Defense agencies and national R&D programs are heavily investing in CIM for secure and energy-efficient computing. Applications in signal intelligence, cryptography, and autonomous defense systems require rapid and power-efficient processing. Government funding accelerates ecosystem development and lowers risks for private industry players. This dual-track demand ensures CIM’s relevance in both commercial and defense markets.

• Integration with AI Accelerators and Cloud Infrastructure
  Cloud providers and AI chip companies are beginning to integrate CIM-based modules into accelerators to enhance efficiency. By reducing latency and energy use in inference workloads, CIM contributes to sustainable datacenter operations. Integration with cloud AI services provides an immediate commercialization pathway for CIM technology. This synergy between edge and cloud adoption broadens CIM’s market potential.

Challenges in the Market

• Precision and Reliability in Analog CIM
  Analog in-memory computing offers high parallelism but struggles with precision due to noise, device variability, and temperature effects. These issues limit the applicability of analog CIM for tasks requiring high accuracy. While digital CIM offers improvements, it sacrifices energy efficiency. Addressing these challenges requires advanced error-correction, calibration, and hybrid architectures, adding to complexity.

• Manufacturability and Scalability Issues
  Integrating CIM into advanced process nodes requires significant engineering effort. Variability in device performance and yield challenges slow down commercialization. Compatibility with CMOS processes is improving, but high-volume manufacturing of CIM arrays remains limited. Scaling production while maintaining consistency across wafers is a key technical hurdle for vendors.

• Endurance and Longevity of Non-Volatile Memories
  While ReRAM, MRAM, and PCM enable persistent CIM, they face endurance limitations compared to conventional DRAM or SRAM. Continuous writes degrade device performance over time, raising reliability concerns for long-term use. Manufacturers must innovate in materials and circuit design to overcome these hurdles before widespread adoption can occur.

• Integration with Software and EDA Toolchains
  Compute-in-memory introduces new programming models that are not fully supported by existing software ecosystems. AI frameworks and EDA tools must evolve to integrate CIM efficiently into system-level designs. Lack of standardized programming abstractions and development kits slows adoption. This gap between hardware innovation and software ecosystem readiness is a major barrier.

• Competition from Alternative Architectures
  While CIM offers compelling benefits, it competes with alternative solutions like neuromorphic computing, near-memory computing, and advanced GPUs/TPUs. These competing technologies also target energy-efficient AI acceleration and receive significant industry support. Vendors must clearly differentiate CIM’s value proposition to sustain adoption in an increasingly crowded hardware innovation space.

Compute-in-Memory Market Segmentation

By Memory Technology

• SRAM-based CIM

• DRAM-based CIM

• ReRAM-based CIM

• MRAM-based CIM

• PCM-based CIM

By Architecture Type

• Analog Compute-in-Memory

• Digital Compute-in-Memory

• Hybrid CIM Architectures

By Application

• AI Inference and Training

• IoT and Edge Computing

• Autonomous Vehicles

• Data Centers and Cloud Computing

• Defense and Aerospace Systems

• Industrial and Consumer Electronics

By End User

• Semiconductor Foundries

• AI Chipmakers

• Consumer Electronics Manufacturers

• Cloud and Datacenter Providers

• Defense Agencies

• Research Institutions

By Region

• North America

• Europe

• Asia-Pacific

• Middle East & Africa

• Latin America

Leading Key Players

• Samsung Electronics

• TSMC

• Intel Corporation

• SK hynix

• Micron Technology

• IBM Research

• GlobalFoundries

• SynSense

• Mythic Inc.

• Applied Materials

Recent Developments

• Samsung Electronics demonstrated a ReRAM-based compute-in-memory prototype achieving over 2x energy efficiency compared to conventional AI accelerators.

• Intel Corporation announced partnerships with research universities to explore hybrid CIM architectures integrating SRAM and ReRAM.

• IBM Research showcased analog CIM arrays with advanced calibration techniques for improved accuracy in AI inference.

• Mythic Inc. expanded commercial deployment of analog CIM chips in edge AI applications such as smart cameras and drones.

• TSMC launched pilot manufacturing processes to support CIM integration at advanced 5nm and 3nm nodes.

This Market Report will Answer the Following Questions

• How many Compute-in-Memory chips are manufactured per annum globally? Who are the sub-component suppliers in different regions?

• Cost Breakdown of a Global Compute-in-Memory chip and Key Vendor Selection Criteria.

• Where is the Compute-in-Memory chip manufactured? What is the average margin per unit?

• Market share of Global Compute-in-Memory manufacturers and their upcoming products.

• Cost advantage for OEMs who manufacture Compute-in-Memory chips in-house.

• Key predictions for the next 5 years in the Global Compute-in-Memory market.

• Average B2B Compute-in-Memory market price in all segments.

• Latest trends in the Compute-in-Memory market, by every market segment.

• The market size (both volume and value) of the Compute-in-Memory market in 2025–2031 and every year in between.

• Production breakup of the Compute-in-Memory market, by suppliers and their OEM relationships.