Global In-memory Computing Chips Market Growth (Status and Outlook) 2026-2032
Description
The global In-memory Computing Chips 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 are computing devices that perform calculations directly within memory arrays or in very close proximity to them, rather than moving data back and forth between separate memory and processing units. By integrating computation into memory, these chips significantly reduce data movement, which lowers power consumption, decreases latency, and alleviates memory bandwidth limitations inherent in traditional von Neumann architectures. In-memory computing chips are particularly well suited for AI and machine-learning workloads dominated by matrix and vector operations, and are typically implemented using SRAM, DRAM, or emerging non-volatile memory technologies, making them a promising solution for energy-efficient edge AI and next-generation computing systems. The downstream market for In-Memory Computing Chips is currently application-driven and highly selective, with adoption concentrated in scenarios where power efficiency, low latency, and memory bandwidth limitations are critical. Key downstream users include edge AI device manufacturers, robotics OEMs, smart cameras, industrial automation vendors, and IoT system integrators, who deploy CIM chips mainly for AI inference, pattern recognition, and real-time signal processing. In data-center environments, CIM solutions are still in exploratory or pilot stages, typically used for specific workloads rather than general-purpose acceleration. Overall, downstream demand is characterized by customized designs, small-to-medium volume orders, and close co-development between chip vendors and system makers, with broader adoption expected as performance stability, precision control, and software toolchains mature.
The In-Memory Computing (IMC) chip market is at an early but accelerating commercialization stage, driven by the growing need to overcome power consumption and memory-bandwidth bottlenecks in AI workloads. Demand is primarily coming from edge AI devices, intelligent sensors, robotics, and low-power inference applications, where energy efficiency and real-time response are more critical than peak general-purpose performance. The supply side is still fragmented, led by startups, research spin-offs, and pilot projects from memory and semiconductor companies, with products mainly appearing as prototypes, test chips, or application-specific accelerators rather than mass-market processors. While conventional GPUs, NPUs, and ASICs remain dominant, IMC chips are increasingly viewed as a complementary architecture for power-constrained scenarios, and broader adoption is expected as manufacturing maturity, accuracy control, and software ecosystems improve toward the late 2020s.
LPI (LP Information)' newest research report, the “In-memory Computing Chips Industry Forecast” looks at past sales and reviews total world In-memory Computing Chips sales in 2025, providing a comprehensive analysis by region and market sector of projected In-memory Computing Chips sales for 2026 through 2032. With In-memory Computing Chips 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 industry.
This Insight Report provides a comprehensive analysis of the global In-memory Computing Chips 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 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 market.
This Insight Report evaluates the key market trends, drivers, and affecting factors shaping the global outlook for In-memory Computing Chips 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.
This report presents a comprehensive overview, market shares, and growth opportunities of In-memory Computing Chips 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 Media:
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 are computing devices that perform calculations directly within memory arrays or in very close proximity to them, rather than moving data back and forth between separate memory and processing units. By integrating computation into memory, these chips significantly reduce data movement, which lowers power consumption, decreases latency, and alleviates memory bandwidth limitations inherent in traditional von Neumann architectures. In-memory computing chips are particularly well suited for AI and machine-learning workloads dominated by matrix and vector operations, and are typically implemented using SRAM, DRAM, or emerging non-volatile memory technologies, making them a promising solution for energy-efficient edge AI and next-generation computing systems. The downstream market for In-Memory Computing Chips is currently application-driven and highly selective, with adoption concentrated in scenarios where power efficiency, low latency, and memory bandwidth limitations are critical. Key downstream users include edge AI device manufacturers, robotics OEMs, smart cameras, industrial automation vendors, and IoT system integrators, who deploy CIM chips mainly for AI inference, pattern recognition, and real-time signal processing. In data-center environments, CIM solutions are still in exploratory or pilot stages, typically used for specific workloads rather than general-purpose acceleration. Overall, downstream demand is characterized by customized designs, small-to-medium volume orders, and close co-development between chip vendors and system makers, with broader adoption expected as performance stability, precision control, and software toolchains mature.
The In-Memory Computing (IMC) chip market is at an early but accelerating commercialization stage, driven by the growing need to overcome power consumption and memory-bandwidth bottlenecks in AI workloads. Demand is primarily coming from edge AI devices, intelligent sensors, robotics, and low-power inference applications, where energy efficiency and real-time response are more critical than peak general-purpose performance. The supply side is still fragmented, led by startups, research spin-offs, and pilot projects from memory and semiconductor companies, with products mainly appearing as prototypes, test chips, or application-specific accelerators rather than mass-market processors. While conventional GPUs, NPUs, and ASICs remain dominant, IMC chips are increasingly viewed as a complementary architecture for power-constrained scenarios, and broader adoption is expected as manufacturing maturity, accuracy control, and software ecosystems improve toward the late 2020s.
LPI (LP Information)' newest research report, the “In-memory Computing Chips Industry Forecast” looks at past sales and reviews total world In-memory Computing Chips sales in 2025, providing a comprehensive analysis by region and market sector of projected In-memory Computing Chips sales for 2026 through 2032. With In-memory Computing Chips 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 industry.
This Insight Report provides a comprehensive analysis of the global In-memory Computing Chips 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 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 market.
This Insight Report evaluates the key market trends, drivers, and affecting factors shaping the global outlook for In-memory Computing Chips 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.
This report presents a comprehensive overview, market shares, and growth opportunities of In-memory Computing Chips 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 Media:
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
111 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 Market Size by Player
- 4 In-memory Computing Chips by Region
- 5 Americas
- 6 APAC
- 7 Europe
- 8 Middle East & Africa
- 9 Market Drivers, Challenges and Trends
- 10 Global In-memory Computing Chips Market Forecast
- 11 Key Players Analysis
- 12 Research Findings and Conclusion
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