Global Leading Market Research Publisher QYResearch announces the release of its latest report “Computing in Memory Technology – Global Market Share and Ranking, Overall Sales and Demand Forecast 2026-2032″. Based on current situation and impact historical analysis (2021-2025) and forecast calculations (2026-2032), this report provides a comprehensive analysis of the global Computing in Memory Technology market, including market size, share, demand, industry development status, and forecasts for the next few years.
The global market for Computing in Memory Technology was estimated to be worth US642millionin2025andisprojectedtoreachUS642millionin2025andisprojectedtoreachUS 7500 million, growing at a CAGR of 42.7% from 2026 to 2032. Industry tracking indicates the global CIM technology market size reached approximately US$268 million in 2024, underscoring the extraordinary growth trajectory ahead as AI workloads and data-intensive applications accelerate adoption.
As a new computing architecture, storage-computing integration is considered to be a revolutionary technology with potential and has received great attention at home and abroad. The core is to fully integrate storage and computing, effectively overcome the bottleneck of the von Neumann architecture, and combine advanced packaging and new storage devices in the post-Moore era to achieve an order of magnitude improvement in computing energy efficiency. According to the distance between storage and computing, the technical solutions of generalized storage-computing integration are divided into three categories, namely, Processing Near Memory (PNM), Processing ln Memory (PlM) and Computing in Memory (CIM). In-memory computing is storage-computing integration in a narrow sense.
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Market Drivers: The Convergence of AI Demand and Architectural Innovation
Explosive Growth in Computing Power Demand. The global AI chip market is expected to reach US$120 billion in 2025, yet approximately 75% of computing power is consumed in data transfer rather than computation itself—a staggering inefficiency that CIM directly addresses. Large-scale language models such as GPT-5, with more than 10 trillion parameters, can see processing-in-memory (PIM) improve sparse matrix operation efficiency by 3-5 times.
The Data Center Energy Consumption Crisis. Global data center power consumption accounts for 1.5% of total electricity demand, with data transfer energy consumption representing 40% in traditional von Neumann architectures. CIM can reduce energy consumption by more than 50% by mitigating the memory wall effect, making it essential for sustainable AI infrastructure.
Moore’s Law Deceleration and Architectural Innovation. The cost of advanced processes below 3nm has soared, while marginal benefits from increased transistor density have diminished. CIM integrates computing units through 3D stacking processes such as HBM3, breaking through planar process limitations. As one industry observer notes, “AI workloads are memory-centric—it’s not the computing that takes time or power, it’s all the moving of data”.
Maturation of New Storage Technologies. Non-volatile memory technologies—including ReRAM, MRAM, and PCM—possess analog computing capabilities naturally suited to CIM architectures. The resistance states of ReRAM can directly participate in matrix operations, while storage-class memory technologies like Intel Optane and Samsung Z-NAND provide high-performance, low-latency storage media.
Edge Computing and IoT Applications
Energy Efficiency Revolution. Devices in autonomous driving, AR/VR, and industrial IoT must process massive data locally. CIM can reduce power consumption by 70% and extend battery life by 2-3 times. Predictive maintenance applications requiring microsecond response times benefit from latency reduction from milliseconds to nanoseconds.
Recent breakthroughs demonstrate commercial viability. MediaTek’s Dimensity 9500 chip has integrated digital compute-in-memory technology, while researchers have developed a fully integrated CIM system using 2D memristors that achieves 97.5% accuracy on pattern recognition tasks at a fraction of traditional energy costs. A 28nm RRAM-based CIM macro has demonstrated 2.82 TOPS/mm² area efficiency with hybrid programming capabilities.
Competitive Landscape
Global key players of Computing in Memory Technology include Syntiant, Zhicun(Witmem) Technology, Reexen Technology, Graphcore and Mythic, etc. The top five players hold a share over 80%. North America is the largest market, has a share about 50%. In terms of product type, In-memory Computing is the largest segment, occupied for a share of about 88%, and in terms of application, Small Computing Power has a share about 90 percent.
Policy Support and Capital Inflows. The US CHIPS Act, the EU’s European Processor Initiative, and China’s 14th Five-Year Plan all list CIM as a key strategic direction. In 2023, global PIM financing exceeded US$5 billion, with industry giants Samsung, SK Hynix, and TSMC accelerating their CIM roadmaps. Start-ups such as Mythic and UPMEM have received multiple financing rounds.
Supply Chain Evolution. Memory manufacturers (Micron, Kioxia) and IP suppliers (Synopsys, Cadence) are collaborating to develop PIM design tool chains, while foundries (SMIC, UMC) are launching 2.5D/3D packaging technologies to support mass production of CIM chips.
Strategic Outlook
The CIM technology market stands at an inflection point, driven by converging forces of AI demand, energy constraints, and hardware innovation. Companies that can master process integration capabilities, algorithm-hardware co-design, and ecosystem development will capture disproportionate value in this rapidly expanding market. Chinese firms must overcome gaps in memory media and EDA tools while accelerating commercialization in AI and edge scenarios. The future of computing is being rewritten in memory—and the race has only just begun.
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