Global In-memory Computing Chips for AI Market Growth (Status and Outlook) 2026-2032
Description
The global In-memory Computing Chips for AI market size is predicted to grow from US$ 226 million in 2025 to US$ 48777 million in 2032; it is expected to grow at a CAGR of 116.5% from 2026 to 2032.
In-memory Computing Chips for AI are specialized chips that perform AI computations directly inside memory arrays or very close to where data is stored, instead of moving data back and forth between memory and a separate processor. By integrating computation—such as multiply-accumulate operations used in neural networks—within memory, these chips significantly reduce data movement, energy consumption, and latency, overcoming the memory bandwidth bottleneck of traditional von Neumann architectures. In-memory computing chips are particularly well suited for AI inference and edge-AI applications, and are commonly implemented using SRAM, DRAM, or emerging non-volatile memory technologies (such as ReRAM or MRAM), offering a promising path toward high-efficiency, low-power AI acceleration. The downstream market for In-Memory Computing (CIM) Chips for AI is currently in an early adoption phase, with demand concentrated in power- and latency-sensitive AI inference scenarios rather than large-scale training. Key downstream users include edge AI device manufacturers, robotics OEMs, smart cameras, industrial automation system integrators, and IoT solution providers, where CIM chips are used to reduce energy consumption and enable real-time processing under tight thermal and power constraints. Deployment is typically project-based or design-win driven, with close collaboration between chip vendors and system customers, and volumes remain limited compared with mainstream GPUs and NPUs. As AI applications expand deeper into embedded, industrial, and always-on systems, downstream demand for CIM chips is expected to broaden, especially in scenarios where conventional architectures struggle to meet power-efficiency requirements.
The In-memory Computing Chips for AI market is at an early but rapidly emerging stage, driven by the growing need for energy-efficient AI inference as traditional GPU- and CPU-centric architectures face power and memory-bandwidth limitations. Current demand is mainly concentrated in edge AI, smart sensors, robotics, automotive, and low-power intelligent devices, where reducing latency and energy consumption is more critical than peak compute performance. The competitive landscape is dominated by startups, university spin-offs, and joint development programs with foundries and memory vendors, while large semiconductor companies are still largely in exploratory or pilot phases. Although large-scale adoption is constrained by challenges in accuracy, reliability, software ecosystem maturity, and manufacturing consistency, industry consensus expects commercial penetration to accelerate after the mid-2020s, with CIM chips first gaining traction in specialized and power-constrained AI applications before broader deployment.
LPI (LP Information)' newest research report, the “In-memory Computing Chips for AI Industry Forecast” looks at past sales and reviews total world In-memory Computing Chips for AI sales in 2025, providing a comprehensive analysis by region and market sector of projected In-memory Computing Chips for AI sales for 2026 through 2032. With In-memory Computing Chips for AI sales broken down by region, market sector and sub-sector, this report provides a detailed analysis in US$ millions of the world In-memory Computing Chips for AI industry.
This Insight Report provides a comprehensive analysis of the global In-memory Computing Chips for AI landscape and highlights key trends related to product segmentation, company formation, revenue, and market share, latest development, and M&A activity. This report also analyses the strategies of leading global companies with a focus on In-memory Computing Chips for AI portfolios and capabilities, market entry strategies, market positions, and geographic footprints, to better understand these firms’ unique position in an accelerating global In-memory Computing Chips for AI market.
This Insight Report evaluates the key market trends, drivers, and affecting factors shaping the global outlook for In-memory Computing Chips for AI and breaks down the forecast by Type, by Application, geography, and market size to highlight emerging pockets of opportunity. With a transparent methodology based on hundreds of bottom-up qualitative and quantitative market inputs, this study forecast offers a highly nuanced view of the current state and future trajectory in the global In-memory Computing Chips for AI.
This report presents a comprehensive overview, market shares, and growth opportunities of In-memory Computing Chips for AI market by product type, application, key players and key regions and countries.
Segmentation by Type:
In-memory Processing (PIM)
In-memory Computation (CIM)
Segmentation by Storage Medium:
DRAM
SRAM
Others
Segmentation by Calculation Method:
Analog CIM
Digital CIM
Segmentation by Application:
Small Computing Power
Large Computing Power
This report also splits the market by region:
Americas
United States
Canada
Mexico
Brazil
APAC
China
Japan
Korea
Southeast Asia
India
Australia
Europe
Germany
France
UK
Italy
Russia
Middle East & Africa
Egypt
South Africa
Israel
Turkey
GCC Countries
The below companies that are profiled have been selected based on inputs gathered from primary experts and analyzing the company's coverage, product portfolio, its market penetration.
Samsung
SK Hynix
Syntiant
D-Matrix
Mythic
Graphcore
EnCharge AI
Axelera AI
Hangzhou Zhicun (Witmem) Technology
Suzhou Yizhu Intelligent Technology
Shenzhen Reexen Technology
Beijing Houmo Technology
AistarTek
Beijing Pingxin Technology
Please note: The report will take approximately 2 business days to prepare and deliver.
In-memory Computing Chips for AI are specialized chips that perform AI computations directly inside memory arrays or very close to where data is stored, instead of moving data back and forth between memory and a separate processor. By integrating computation—such as multiply-accumulate operations used in neural networks—within memory, these chips significantly reduce data movement, energy consumption, and latency, overcoming the memory bandwidth bottleneck of traditional von Neumann architectures. In-memory computing chips are particularly well suited for AI inference and edge-AI applications, and are commonly implemented using SRAM, DRAM, or emerging non-volatile memory technologies (such as ReRAM or MRAM), offering a promising path toward high-efficiency, low-power AI acceleration. The downstream market for In-Memory Computing (CIM) Chips for AI is currently in an early adoption phase, with demand concentrated in power- and latency-sensitive AI inference scenarios rather than large-scale training. Key downstream users include edge AI device manufacturers, robotics OEMs, smart cameras, industrial automation system integrators, and IoT solution providers, where CIM chips are used to reduce energy consumption and enable real-time processing under tight thermal and power constraints. Deployment is typically project-based or design-win driven, with close collaboration between chip vendors and system customers, and volumes remain limited compared with mainstream GPUs and NPUs. As AI applications expand deeper into embedded, industrial, and always-on systems, downstream demand for CIM chips is expected to broaden, especially in scenarios where conventional architectures struggle to meet power-efficiency requirements.
The In-memory Computing Chips for AI market is at an early but rapidly emerging stage, driven by the growing need for energy-efficient AI inference as traditional GPU- and CPU-centric architectures face power and memory-bandwidth limitations. Current demand is mainly concentrated in edge AI, smart sensors, robotics, automotive, and low-power intelligent devices, where reducing latency and energy consumption is more critical than peak compute performance. The competitive landscape is dominated by startups, university spin-offs, and joint development programs with foundries and memory vendors, while large semiconductor companies are still largely in exploratory or pilot phases. Although large-scale adoption is constrained by challenges in accuracy, reliability, software ecosystem maturity, and manufacturing consistency, industry consensus expects commercial penetration to accelerate after the mid-2020s, with CIM chips first gaining traction in specialized and power-constrained AI applications before broader deployment.
LPI (LP Information)' newest research report, the “In-memory Computing Chips for AI Industry Forecast” looks at past sales and reviews total world In-memory Computing Chips for AI sales in 2025, providing a comprehensive analysis by region and market sector of projected In-memory Computing Chips for AI sales for 2026 through 2032. With In-memory Computing Chips for AI sales broken down by region, market sector and sub-sector, this report provides a detailed analysis in US$ millions of the world In-memory Computing Chips for AI industry.
This Insight Report provides a comprehensive analysis of the global In-memory Computing Chips for AI landscape and highlights key trends related to product segmentation, company formation, revenue, and market share, latest development, and M&A activity. This report also analyses the strategies of leading global companies with a focus on In-memory Computing Chips for AI portfolios and capabilities, market entry strategies, market positions, and geographic footprints, to better understand these firms’ unique position in an accelerating global In-memory Computing Chips for AI market.
This Insight Report evaluates the key market trends, drivers, and affecting factors shaping the global outlook for In-memory Computing Chips for AI and breaks down the forecast by Type, by Application, geography, and market size to highlight emerging pockets of opportunity. With a transparent methodology based on hundreds of bottom-up qualitative and quantitative market inputs, this study forecast offers a highly nuanced view of the current state and future trajectory in the global In-memory Computing Chips for AI.
This report presents a comprehensive overview, market shares, and growth opportunities of In-memory Computing Chips for AI market by product type, application, key players and key regions and countries.
Segmentation by Type:
In-memory Processing (PIM)
In-memory Computation (CIM)
Segmentation by Storage Medium:
DRAM
SRAM
Others
Segmentation by Calculation Method:
Analog CIM
Digital CIM
Segmentation by Application:
Small Computing Power
Large Computing Power
This report also splits the market by region:
Americas
United States
Canada
Mexico
Brazil
APAC
China
Japan
Korea
Southeast Asia
India
Australia
Europe
Germany
France
UK
Italy
Russia
Middle East & Africa
Egypt
South Africa
Israel
Turkey
GCC Countries
The below companies that are profiled have been selected based on inputs gathered from primary experts and analyzing the company's coverage, product portfolio, its market penetration.
Samsung
SK Hynix
Syntiant
D-Matrix
Mythic
Graphcore
EnCharge AI
Axelera AI
Hangzhou Zhicun (Witmem) Technology
Suzhou Yizhu Intelligent Technology
Shenzhen Reexen Technology
Beijing Houmo Technology
AistarTek
Beijing Pingxin Technology
Please note: The report will take approximately 2 business days to prepare and deliver.
Table of Contents
103 Pages
- *This is a tentative TOC and the final deliverable is subject to change.*
- 1 Scope of the Report
- 2 Executive Summary
- 3 In-memory Computing Chips for AI Market Size by Player
- 4 In-memory Computing Chips for AI by Region
- 5 Americas
- 6 APAC
- 7 Europe
- 8 Middle East & Africa
- 9 Market Drivers, Challenges and Trends
- 10 Global In-memory Computing Chips for AI Market Forecast
- 11 Key Players Analysis
- 12 Research Findings and Conclusion
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