Global Compute-in-Memory AI Accelerator Chips Market 2026 by Company, Regions, Type and Application, Forecast to 2032
According to our (Global Info Research) latest study, the global Compute-in-Memory AI Accelerator Chips market size was valued at US$ 238 million in 2025 and is forecast to a readjusted size of US$ 42322 million by 2032 with a CAGR of 109.7% during review period.
Compute-in-Memory (CIM) AI Accelerator Chips are specialized hardware accelerators that execute AI computations directly within or adjacent to memory arrays, enabling operations such as multiply–accumulate (MAC) to be performed where data is stored. By drastically reducing data movement between memory and processing units, CIM accelerator chips lower power consumption, reduce latency, and alleviate memory bandwidth bottlenecks associated with conventional von Neumann architectures. These chips are particularly effective for AI inference workloads dominated by matrix and vector operations, and are commonly implemented using SRAM, DRAM, or emerging non-volatile memories (e.g., ReRAM, MRAM), making them a key technology path for energy-efficient edge AI and next-generation AI computing systems.
This report is a detailed and comprehensive analysis for global Compute-in-Memory AI Accelerator Chips market. Both quantitative and qualitative analyses are presented by company, by region & country, by Type and by Application. As the market is constantly changing, this report explores the competition, supply and demand trends, as well as key factors that contribute to its changing demands across many markets. Company profiles and product examples of selected competitors, along with market share estimates of some of the selected leaders for the year 2025, are provided.
Key Features:
Global Compute-in-Memory AI Accelerator Chips market size and forecasts, in consumption value ($ Million), 2021-2032
Global Compute-in-Memory AI Accelerator Chips market size and forecasts by region and country, in consumption value ($ Million), 2021-2032
Global Compute-in-Memory AI Accelerator Chips market size and forecasts, by Type and by Application, in consumption value ($ Million), 2021-2032
Global Compute-in-Memory AI Accelerator Chips market shares of main players, in revenue ($ Million), 2021-2026
The Primary Objectives in This Report Are:
To determine the size of the total market opportunity of global and key countries
To assess the growth potential for Compute-in-Memory AI Accelerator Chips
To forecast future growth in each product and end-use market
To assess competitive factors affecting the marketplace
This report profiles key players in the global Compute-in-Memory AI Accelerator Chips market based on the following parameters - company overview, revenue, gross margin, product portfolio, geographical presence, and key developments. Key companies covered as a part of this study include Samsung, SK Hynix, Syntiant, D-Matrix, Mythic, Graphcore, EnCharge AI, Axelera AI, Hangzhou Zhicun (Witmem) Technology, Suzhou Yizhu Intelligent Technology, etc.
This report also provides key insights about market drivers, restraints, opportunities, new product launches or approvals.
Market segmentation
Compute-in-Memory AI Accelerator Chips market is split by Type and by Application. For the period 2021-2032, the growth among segments provides accurate calculations and forecasts for Consumption Value by Type and by Application. This analysis can help you expand your business by targeting qualified niche markets.
Market segment by Type
North America (United States, Canada and Mexico)
Europe (Germany, France, UK, Russia, Italy and Rest of Europe)
Asia-Pacific (China, Japan, South Korea, India, Southeast Asia and Rest of Asia-Pacific)
South America (Brazil, Rest of South America)
Middle East & Africa (Turkey, Saudi Arabia, UAE, Rest of Middle East & Africa)
The content of the study subjects, includes a total of 13 chapters:
Chapter 1, to describe Compute-in-Memory AI Accelerator Chips product scope, market overview, market estimation caveats and base year.
Chapter 2, to profile the top players of Compute-in-Memory AI Accelerator Chips, with revenue, gross margin, and global market share of Compute-in-Memory AI Accelerator Chips from 2021 to 2026.
Chapter 3, the Compute-in-Memory AI Accelerator Chips competitive situation, revenue, and global market share of top players are analyzed emphatically by landscape contrast.
Chapter 4 and 5, to segment the market size by Type and by Application, with consumption value and growth rate by Type, by Application, from 2021 to 2032.
Chapter 6, 7, 8, 9, and 10, to break the market size data at the country level, with revenue and market share for key countries in the world, from 2021 to 2026.and Compute-in-Memory AI Accelerator Chips market forecast, by regions, by Type and by Application, with consumption value, from 2027 to 2032.
Chapter 11, market dynamics, drivers, restraints, trends, Porters Five Forces analysis.
Chapter 12, the key raw materials and key suppliers, and industry chain of Compute-in-Memory AI Accelerator Chips.
Chapter 13, to describe Compute-in-Memory AI Accelerator Chips research findings and conclusion.
Compute-in-Memory (CIM) AI Accelerator Chips are specialized hardware accelerators that execute AI computations directly within or adjacent to memory arrays, enabling operations such as multiply–accumulate (MAC) to be performed where data is stored. By drastically reducing data movement between memory and processing units, CIM accelerator chips lower power consumption, reduce latency, and alleviate memory bandwidth bottlenecks associated with conventional von Neumann architectures. These chips are particularly effective for AI inference workloads dominated by matrix and vector operations, and are commonly implemented using SRAM, DRAM, or emerging non-volatile memories (e.g., ReRAM, MRAM), making them a key technology path for energy-efficient edge AI and next-generation AI computing systems.
This report is a detailed and comprehensive analysis for global Compute-in-Memory AI Accelerator Chips market. Both quantitative and qualitative analyses are presented by company, by region & country, by Type and by Application. As the market is constantly changing, this report explores the competition, supply and demand trends, as well as key factors that contribute to its changing demands across many markets. Company profiles and product examples of selected competitors, along with market share estimates of some of the selected leaders for the year 2025, are provided.
Key Features:
Global Compute-in-Memory AI Accelerator Chips market size and forecasts, in consumption value ($ Million), 2021-2032
Global Compute-in-Memory AI Accelerator Chips market size and forecasts by region and country, in consumption value ($ Million), 2021-2032
Global Compute-in-Memory AI Accelerator Chips market size and forecasts, by Type and by Application, in consumption value ($ Million), 2021-2032
Global Compute-in-Memory AI Accelerator Chips market shares of main players, in revenue ($ Million), 2021-2026
The Primary Objectives in This Report Are:
To determine the size of the total market opportunity of global and key countries
To assess the growth potential for Compute-in-Memory AI Accelerator Chips
To forecast future growth in each product and end-use market
To assess competitive factors affecting the marketplace
This report profiles key players in the global Compute-in-Memory AI Accelerator Chips market based on the following parameters - company overview, revenue, gross margin, product portfolio, geographical presence, and key developments. Key companies covered as a part of this study include Samsung, SK Hynix, Syntiant, D-Matrix, Mythic, Graphcore, EnCharge AI, Axelera AI, Hangzhou Zhicun (Witmem) Technology, Suzhou Yizhu Intelligent Technology, etc.
This report also provides key insights about market drivers, restraints, opportunities, new product launches or approvals.
Market segmentation
Compute-in-Memory AI Accelerator Chips market is split by Type and by Application. For the period 2021-2032, the growth among segments provides accurate calculations and forecasts for Consumption Value by Type and by Application. This analysis can help you expand your business by targeting qualified niche markets.
Market segment by Type
- Near-in-memory Computation (PNM)
- In-memory Processing (PIM)
- In-memory Computation (CIM)
- SRAM-based CIM
- DRAM-based PIM
- NVM-based CIM
- Small Computing Power
- Large Computing Power
- 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
North America (United States, Canada and Mexico)
Europe (Germany, France, UK, Russia, Italy and Rest of Europe)
Asia-Pacific (China, Japan, South Korea, India, Southeast Asia and Rest of Asia-Pacific)
South America (Brazil, Rest of South America)
Middle East & Africa (Turkey, Saudi Arabia, UAE, Rest of Middle East & Africa)
The content of the study subjects, includes a total of 13 chapters:
Chapter 1, to describe Compute-in-Memory AI Accelerator Chips product scope, market overview, market estimation caveats and base year.
Chapter 2, to profile the top players of Compute-in-Memory AI Accelerator Chips, with revenue, gross margin, and global market share of Compute-in-Memory AI Accelerator Chips from 2021 to 2026.
Chapter 3, the Compute-in-Memory AI Accelerator Chips competitive situation, revenue, and global market share of top players are analyzed emphatically by landscape contrast.
Chapter 4 and 5, to segment the market size by Type and by Application, with consumption value and growth rate by Type, by Application, from 2021 to 2032.
Chapter 6, 7, 8, 9, and 10, to break the market size data at the country level, with revenue and market share for key countries in the world, from 2021 to 2026.and Compute-in-Memory AI Accelerator Chips market forecast, by regions, by Type and by Application, with consumption value, from 2027 to 2032.
Chapter 11, market dynamics, drivers, restraints, trends, Porters Five Forces analysis.
Chapter 12, the key raw materials and key suppliers, and industry chain of Compute-in-Memory AI Accelerator Chips.
Chapter 13, to describe Compute-in-Memory AI Accelerator Chips research findings and conclusion.
