Compute-in-Memory(CIM) 및 Processing-in-Memory(PIM) 시장 : 점유율 분석, 업계 동향과 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the compute in memory (CIM) and processing in memory (PIM) market size is projected to be USD 0.81 billion in 2025, USD 1.16 billion in 2026, and reach USD 6.54 billion by 2031, growing at a CAGR of 41.33% from 2026 to 2031.

This report is Segmented by Component (Hardware, Software Stack, and Services), Memory Technology (SRAM, DRAM, HBM, MRAM, RRAM/ReRAM, and PCM), Application (AI and ML, Edge AI, Data Centers, Automotive and ADAS, Industrial Automation, and Internet of Things (IoT)), and Geography (North America, Europe, Asia-Pacific, Rest of the World). Market Forecasts are in Value (USD).

Global Compute In Memory (CIM) and Processing In Memory (PIM) Market Trends and Insights

AI Workloads Are Hitting the Memory Wall

The compute in memory (CIM) and processing in memory (PIM) market is driven by a growing mismatch between processor throughput and memory bandwidth in AI systems. Energy use now depends heavily on data movement, not just arithmetic throughput, making memory proximity a practical design priority for AI infrastructure. This matters most in large inference environments, where repeated memory access can limit performance even when accelerator capacity is available. The compute in memory (CIM) and processing in memory (PIM) market, therefore, benefits from a lower adoption hurdle, as these architectures do not need to replace every accelerator in the stack to gain traction. They can instead take on memory-bound operations that conventional architectures handle less efficiently. That creates room for earlier deployment in AI servers, enterprise inference systems, and future mixed-architecture platforms.

Cloud-Native Analytics Is Pushing Compute Closer To Data

The compute in memory (CIM) and processing in memory (PIM) market is also being driven by cloud operators seeking better performance per watt from real-time inference infrastructure. NVIDIA stated that full-stack optimization is becoming central to AI factory efficiency, which supports the case for architectures that reduce repeated traffic across the memory interface. Research presented at IEEE ASICON 2025 showed that a hybrid DRAM-PIM and SRAM-CIM design for transformer inference delivered a 1.51x speedup and 1.24x energy reduction compared to a DRAM-PIM-only baseline for Llama2-7B at 28nm. That result is important because it shows that complementary memory technologies can be mapped to different model functions rather than forced into a single architecture. In June 2026, Micron and Anthropic announced a strategic agreement that combined a multi-year memory and storage supply commitment with a strategic investment, showing that memory roadmaps are now being tied directly to future AI infrastructure planning. This kind of alignment supports faster commercialization of the compute in memory (CIM) and processing in memory (PIM) market because infrastructure buyers and memory suppliers share common deployment timelines.

Limited Toolchains And Compiler Support For PIM Adoption

The compute in memory (CIM) and processing in memory (PIM) market still faces a major software constraint because application porting requires different programming models from conventional accelerator environments. ETH Zurich and the SAFARI Research Group described the need for new tools, programming models, and system support for processing-in-memory architectures, which shows that the software layer is still catching up with the hardware opportunity. A 2026 compiler study from Politecnico di Milano also found that performance gains can vary strongly by workload and that general-purpose ports may deliver limited benefit without deeper code adaptation. This means adoption is not only about buying new silicon; enterprises also need compilers, APIs, runtime layers, and validated mapping flows. The compute in memory (CIM) and processing in memory (PIM) market could still grow quickly under these conditions, but commercial scale will remain easier in tightly controlled environments than in broad open software ecosystems. That is why software maturity is becoming one of the clearest dividing lines between promising hardware and deployable platforms.

Other drivers and restraints analyzed in the detailed report include:

1. Edge Inference Is Raising The Value Of Localized Compute
2. Sub-10nm And 3D-Stacked Memory Roadmaps Are Improving CIM Feasibility
3. Yield, Test, And Packaging Complexity In Heterogeneous Memory-Logic Integration

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

CIM and PIM hardware accounted for 83.45% of revenue in 2025, indicating that early spending in the compute in memory (CIM) and processing in memory (PIM) market remained focused on silicon and platform buildout. This pattern reflects the capital intensity of chip design, packaging, and memory integration, especially when demand still comes largely from AI infrastructure programs and specialist system developers. The hardware lead also aligns with the current stage of commercialization, where customers are validating core architectures before large-scale software standardization becomes feasible. At the same time, the revenue mix points to a market that is still building out its installed base rather than one that has already matured into a broad recurring software model. That installed base is important because it creates the technical footprint on which later software and service revenue can expand.

The software stack segment is forecast to grow at 41.56% through 2031, making it the fastest-growing component of the compute in memory (CIM) and processing in memory (PIM) market. That growth reflects rising demand for compilers, model mapping tools, hardware abstraction layers, and deployment frameworks that can connect specialized memory-centric hardware to production AI workloads. The same trend is visible in research activity, where software support is becoming necessary for analog and digital platforms to move beyond laboratory configurations. Services remain smaller, yet they carry real strategic value because many enterprises lack the internal expertise needed to integrate new memory architectures into automotive, industrial, and safety-sensitive systems. Over time, the compute in memory (CIM) and processing in memory (PIM) industry is likely to shift from hardware-led revenue to a fuller platform economics model, but the current mix shows that silicon still anchors commercial demand.

Complete Report Scope:

• By Component
  • CIM/PIM Hardware
  • Software Stack
  • Services
• By Memory Technology
  • SRAM-Based CIM
  • DRAM-Based PIM
  • HBM-Based PIM
  • MRAM-Based CIM
  • RRAM/ReRAM-Based CIM
  • PCM-Based CIM
• By Application
  • Artificial Intelligence and Machine Learning
  • Edge AI and Embedded Intelligence
  • Data Centers and Hyperscale AI Infrastructure
  • Automotive and ADAS
  • Industrial Automation and Robotics
  • Internet of Things (IoT)
  • Other Applications
• By Geography
  • North America
  • Europe
  • Asia Pacific
    • China
    • Japan
    • South Korea
    • Taiwan
    • Rest of Asia Pacific
  • Rest of the World

Geography Analysis

North America held 42.77% of the compute in memory (CIM) and processing in memory (PIM) market share in 2025, making it the largest regional base. This position is tied to the concentration of hyperscale AI infrastructure, advanced memory investment, and close links between model developers and semiconductor suppliers. In July 2026, Micron announced that it had accelerated planned U.S. investments to more than USD 250 billion through 2035 and that it had completed the first concrete pour at its Clay, New York, site more than a quarter ahead of schedule. North American operators are also placing greater emphasis on performance per watt, which strengthens the commercial case for architectures that reduce repeated movement across the memory interface. The compute in memory (CIM) and processing in memory (PIM) market in this region, therefore, benefits from both capital depth and a direct infrastructure need.

Asia-Pacific is projected to grow at 42.14% through 2031, making it the fastest-growing geography in the compute in memory (CIM) and processing in memory (PIM) market. The region combines foundry leadership, advanced packaging capability, high-bandwidth memory production, and active public support for semiconductor research. TSMC's June 2026 technology update outlined a CoWoS roadmap extending to larger integration footprints and a SoIC path toward tighter die stackingboth of, both of which are important for chiplet-based memory-centric designs. Samsung Semiconductor's HBM4E sample shipment further reinforced Asia-Pacific's central role in the execution of the n commercial memory roadmap. Japan adds a distinct automotive and edge dimension through public research backing, including NEDO-funded CMOS and spintronics MRAM work and University of Tokyo research on durable ReRAM-based CiM.

Europe and the rest of the world remain smaller in current revenue, but they still matter to the compute in memory (CIM) and processing in memory (PIM) market because they add important automotive, industrial, and defense-oriented demand paths. Europe's activity is concentrated in edge AI, automotive electronics, and research-linked commercialization. Mythic announced in May 2026 that it had acquired Videantis, a German digital processor IP company with broad automotive penetration, to combine analog compute-in-memory with digital processing in a hybrid platform. That move gives the compute in memory (CIM) and processing in memory (PIM) market a clearer route into European automotive and robotics programs through an existing processor IP footprint.

1. Samsung Electronics Co., Ltd.
2. SK hynix Inc.
3. Micron Technology, Inc.
4. IBM Corporation
5. Intel Corporation
6. TSMC
7. Qualcomm Incorporated
8. NVIDIA Corporation
9. Analog Devices, Inc.
10. Renesas Electronics Corporation
11. GSI Technology, Inc.
12. Mythic Inc.
13. TetraMem Inc.
14. SynSense AG
15. BrainChip Holdings Ltd
16. SAP SE
17. Oracle Corporation
18. Microsoft Corporation
19. Amazon Web Services, Inc.
20. Redis Ltd.
21. GridGain Systems, Inc.
22. GigaSpaces Technologies Ltd.
23. Fujitsu Limited
24. Hewlett Packard Enterprise Company

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 Study Assumptions and Market Definition
• 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

• 4.1 Market Overview
• 4.2 Market Drivers
  • 4.2.1 AI Workloads Are Hitting the Memory Wall
  • 4.2.2 Cloud-Native Analytics Is Pushing Compute Closer To Data
  • 4.2.3 Edge Inference Is Raising The Value Of Localized Compute
  • 4.2.4 Sub-10nm And 3D-Stacked Memory Roadmaps Are Improving CIM Feasibility
  • 4.2.5 Power Efficiency Pressure In Data Centers And Edge Devices
  • 4.2.6 Neuromorphic And Sparse Computing Use Cases Are Expanding The TAM Beyond Conventional Analytics
• 4.3 Market Restraints
  • 4.3.1 Limited Toolchains And Compiler Support For PIM Adoption
  • 4.3.2 Yield, Test, And Packaging Complexity In Heterogeneous Memory-Logic Integration
  • 4.3.3 Fragmented Standards Across Memory, Interconnect, And Software Stacks
  • 4.3.4 High Qualification Risk For Mission-Critical Enterprise And Automotive Use Cases
• 4.4 Industry Supply Chain Analysis
• 4.5 Impact of Macroeconomic Factors on the Market
• 4.6 Regulatory Landscape
• 4.7 Technological Outlook
• 4.8 Porter's Five Forces Analysis
  • 4.8.1 Bargaining Power of Suppliers
  • 4.8.2 Bargaining Power of Buyers
  • 4.8.3 Threat of New Entrants
  • 4.8.4 Threat of Substitutes
  • 4.8.5 Intensity of Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Component
  • 5.1.1 CIM/PIM Hardware
  • 5.1.2 Software Stack
  • 5.1.3 Services
• 5.2 By Memory Technology
  • 5.2.1 SRAM-Based CIM
  • 5.2.2 DRAM-Based PIM
  • 5.2.3 HBM-Based PIM
  • 5.2.4 MRAM-Based CIM
  • 5.2.5 RRAM/ReRAM-Based CIM
  • 5.2.6 PCM-Based CIM
• 5.3 By Application
  • 5.3.1 Artificial Intelligence and Machine Learning
  • 5.3.2 Edge AI and Embedded Intelligence
  • 5.3.3 Data Centers and Hyperscale AI Infrastructure
  • 5.3.4 Automotive and ADAS
  • 5.3.5 Industrial Automation and Robotics
  • 5.3.6 Internet of Things (IoT)
  • 5.3.7 Other Applications
• 5.4 By Geography
  • 5.4.1 North America
  • 5.4.2 Europe
  • 5.4.3 Asia Pacific
    • 5.4.3.1 China
    • 5.4.3.2 Japan
    • 5.4.3.3 South Korea
    • 5.4.3.4 Taiwan
    • 5.4.3.5 Rest of Asia Pacific
  • 5.4.4 Rest of the World

6 COMPETITIVE LANDSCAPE

• 6.1 Market Concentration
• 6.2 Strategic Moves
• 6.3 Market Share Analysis
• 6.4 Company Profiles (includes Global Level Overview, Market Level Overview, Core Segments, Financials as available, Strategic Information, Products and Services, Recent Developments)
  • 6.4.1 Samsung Electronics Co., Ltd.
  • 6.4.2 SK hynix Inc.
  • 6.4.3 Micron Technology, Inc.
  • 6.4.4 IBM Corporation
  • 6.4.5 Intel Corporation
  • 6.4.6 TSMC
  • 6.4.7 Qualcomm Incorporated
  • 6.4.8 NVIDIA Corporation
  • 6.4.9 Analog Devices, Inc.
  • 6.4.10 Renesas Electronics Corporation
  • 6.4.11 GSI Technology, Inc.
  • 6.4.12 Mythic Inc.
  • 6.4.13 TetraMem Inc.
  • 6.4.14 SynSense AG
  • 6.4.15 BrainChip Holdings Ltd
  • 6.4.16 SAP SE
  • 6.4.17 Oracle Corporation
  • 6.4.18 Microsoft Corporation
  • 6.4.19 Amazon Web Services, Inc.
  • 6.4.20 Redis Ltd.
  • 6.4.21 GridGain Systems, Inc.
  • 6.4.22 GigaSpaces Technologies Ltd.
  • 6.4.23 Fujitsu Limited
  • 6.4.24 Hewlett Packard Enterprise Company

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment