Compute in memory

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Computing with memory

Computing with memory (or in-memory computing, IMC) is a paradigm that performs computations directly within memory arrays (RAM, RRAM, PCM) rather than transferring data to a separate CPU, significantly reducing energy consumption and latency. It addresses the von Neumann bottleneck by enabling data-parallel operations, such as for neural networks and big data analytics, at speeds up to thousands of times faster than traditional storage methods. These paradigms can follow either a purely spatial computing model or a temporal computing model. The latter approach aims at reducing overheads in FPGAs. Computing with memory differs from computing in memory or processor-in-memory concepts.

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• GSI Technology

  Definition Compute-in-memory (CIM) conceptually means doing compute directly in the memory. As will be discussed in detail in subsequent sections, the memory itself is capable of performing efficient computing, which incurs much less data transfers resulting in higher performance and much lower power. These characteristics make it very beneficial for AI and high-performance computing workloads. Sometimes In-memory compute (IMC) is used for this definition also, but as will be explained in detail care must be observed to distinguish some of the uses of this term with a more appropriate definition of compute-near-memory which has different characteristics. [...] Compute-in-memory represents a significant shift in computing paradigm with proven SRAM technology that is ready for high volume deployment. As the cost of compute and memory—in terms of energy, cooling, and diminishing returns on scale—continues to increase and the volume of data generated by businesses grows, compute-in-memory is becoming an increasingly vital component of modern IT infrastructure, enabling applications and actions that were previously economically unattainable. This whitepaper serves to clearly describe a true digital compute-in-memory architecture and contrast it with alternative architectures, particularly near-memory and PIM. We would like to leave you with a simple checklist or test to determine whether the architecture is true compute-in-memory. If the [...] ### Analog CIM (Compute-In-Memory) Analog compute-in-memory leverages designed memory elements by using tunable resistors, such as Memristors, to perform computations directly inside the memory array itself. This architecture can be called true compute-in-memory (CIM) and can deliver computational efficiency, but there are many technical challenges with this approach as described below. Analog CIM has been explored by many research labs for a long time, and startups have started creating test parts for the direction, but due to challenges described below, this approach has hurdles to becoming mainstream in near future. See Figure 8. Figure 8: Computing Architectures The limitations of analog compute-in-memory architecture include the following:

• Compute-In-Memory (CIM) - ESP on CB Insights

  ## What is Compute-In-Memory (CIM)? The compute-in-memory (CIM) market (also called processor-in-memory, in-compute memory, in-memory compute, or computational memory) combines data storage and computational processing within the same memory unit. Instead of the traditional model where data is transferred between separate processing and storage units, CIM performs computations where the data is stored. These solutions significantly reduce data movement, increasing processing speed and energy efficiency for data centers and high-performance computing applications. Key features include specialized processors for AI tasks, enhanced energy efficiency, and software tool chains for customizable digital accelerators. Expert Collections Subscribe for more information Market Map [...] ## What is Compute-In-Memory (CIM)? The compute-in-memory (CIM) market (also called processor-in-memory, in-compute memory, in-memory compute, or computational memory) combines data storage and computational processing within the same memory unit. Instead of the traditional model where data is transferred between separate processing and storage units, CIM performs computations where the data is stored. These solutions significantly reduce data movement, increasing processing speed and energy efficiency for data centers and high-performance computing applications. Key features include specialized processors for AI tasks, enhanced energy efficiency, and software tool chains for customizable digital accelerators. Expert Collections Subscribe for more information Market Map [...] Analyst Briefing Submitted XCENA is a fabless semiconductor company that designs data-centric computing solutions for the data center industry. The company offers memory solutions that aim to overcome the memory wall bottleneck by memory bandwidth and capacity through its data domain-specific architecture. It serves database analytics, artificial intelligence (AI) data preprocessing, and NGS DNA analysis. It was formerly known as MetisX and changed its name to XCENA in December 2024. The company was founded in 2022 and is based in Yongin-si, South Korea. ### Known Partners Subscribe Our Methodology The ESP matrix leverages data and analyst insight to identify and rank leading private-market companies in a given technology landscape. ## What is Compute-In-Memory (CIM)?

• Compute-In-Memory (CIM) - ESP on CB Insights

  ## What is Compute-In-Memory (CIM)? The compute-in-memory (CIM) market (also called processor-in-memory, in-compute memory, in-memory compute, or computational memory) combines data storage and computational processing within the same memory unit. Instead of the traditional model where data is transferred between separate processing and storage units, CIM performs computations where the data is stored. These solutions significantly reduce data movement, increasing processing speed and energy efficiency for data centers and high-performance computing applications. Key features include specialized processors for AI tasks, enhanced energy efficiency, and software tool chains for customizable digital accelerators. Expert Collections Subscribe for more information Market Map [...] ## What is Compute-In-Memory (CIM)? The compute-in-memory (CIM) market (also called processor-in-memory, in-compute memory, in-memory compute, or computational memory) combines data storage and computational processing within the same memory unit. Instead of the traditional model where data is transferred between separate processing and storage units, CIM performs computations where the data is stored. These solutions significantly reduce data movement, increasing processing speed and energy efficiency for data centers and high-performance computing applications. Key features include specialized processors for AI tasks, enhanced energy efficiency, and software tool chains for customizable digital accelerators. Expert Collections Subscribe for more information Market Map [...] Analyst Briefing Submitted XCENA is a fabless semiconductor company that designs data-centric computing solutions for the data center industry. The company offers memory solutions that aim to overcome the memory wall bottleneck by memory bandwidth and capacity through its data domain-specific architecture. It serves database analytics, artificial intelligence (AI) data preprocessing, and NGS DNA analysis. It was formerly known as MetisX and changed its name to XCENA in December 2024. The company was founded in 2022 and is based in Yongin-si, South Korea. ### Known Partners Subscribe Our Methodology The ESP matrix leverages data and analyst insight to identify and rank leading private-market companies in a given technology landscape. ## What is Compute-In-Memory (CIM)?

• In-memory processing - Wikipedia

  1. In computer science, in-memory processing, also called compute-in-memory (CIM) or processing-in-memory (PIM), is a computer architecture in which data operations are available directly on the data memory, rather than having to be transferred to CPU registers first. This may improve the power usage and performance of moving data between the processor and the main memory. 2. In software engineering, in-memory processing is a software architecture where a database is kept entirely in random-access memory (RAM) or flash memory so that usual accesses, in particular read or query operations, do not require access to disk storage. This may allow faster data operations such as joins "Join (SQL)"), and faster reporting and decision-making in business.

• In-Memory Computation Explained | Hazelcast

  Back to Foundations # What is In-Memory Computation? In-memory computation (or in-memory computing) is the technique of running computer calculations entirely in computer memory (e.g., in RAM). This term typically implies large-scale, complex calculations which require specialized systems software to run the calculations on computers working together in a cluster. As a cluster, the computers pool together their RAM so the calculation is essentially run across computers and leverages the collective RAM space of all the computers together. With in-memory computing, computers pool their RAM together into a shared memory space. With in-memory computing, computers pool their RAM together into a shared memory space. [...] ## How Does In-Memory Computation Work? In-memory computation works by eliminating all slow data accesses and relying exclusively on data stored in RAM. Overall computation performance is greatly improved by removing the latency commonly seen when accessing hard disk drives or SSDs. Software running on one or more computers manages the computation as well as the data in memory, and in the case of multiple computers, the software divides the computation into smaller tasks which are distributed out to each computer to run in parallel. In-memory computation is often done in the technology known as in-memory data grids (IMDG). One such example is Hazelcast IMDG, which lets users run complex computations on large data sets across a cluster of hardware servers while maintaining extreme speed. [...] ## Example Use Cases One use case of in-memory computing is the modeling of complex systems using a large set of related data points. For example, the modeling of weather data is used to help understand the current trends around events such as storms. Millions or even billions of data points are captured to represent various weather-related measurements, upon which calculations are run to identify the likely patterns the storms will take. With so much data to process, eliminating slow disk accesses will help run the calculations in a much shorter time window.