Memory Centric Computing

- Overview

Memory-centric computing, also known as processing in memory (PIM), is a computer architecture paradigm that allows data processing to take place in, near, or on devices that store or generate data. This paradigm can make computing more efficient by offering an alternative to the traditional processor-centric paradigm, which only performs data processing in the processor. 

Memory-centric computing aims to enable computation in and near all places where data is stored and generated. For example, data processing could take place near sensors, memory, or storage devices. 

The "memory wall" problem was identified in the 1990s to describe the issue of microprocessor performance improving faster than DRAM memory speed. This narrowing gap between the speed of CPU and memory means that processor cores will increasingly sit idle waiting for data. 

Memory-centric computing can improve complex event processing, deliver faster reporting and product releases, and provide quicker and more accurate decision-making.

- Memory (Bandwidth and Capacity) Wall

The "memory wall" problem, originally posed by Wulf and McKee in the 1990s, states that microprocessor performance is increasing much faster than DRAM memory speed. This trend makes the memory subsystem one of the most critical system-level performance bottlenecks. 

In addition to the memory "bandwidth" wall, computer system designers are also noticing the emergence of a new memory wall in the data center, one of memory "capacity," where peak imbalances in compute versus memory capacity require hyperscalers to overprovision each server's memory is sized for worst-case usage, resulting in significant memory underutilization. 

To overcome the memory wall problem, computer architects have conducted two important studies, one is memory processing (to solve the problem of memory bandwidth) and the other is memory decomposition (to solve the problem of memory capacity).