列表在开发CIM/PIM 架构的公司

来源: 于 2025-11-03 12:17:39 [博客] [旧帖] [给我悄悄话] 本文已被阅读：次

Compute-in-Memory (CIM) and Processing-in-Memory (PIM) are widely seen as the true architectural leap beyond GPUs, because they attack the single biggest bottleneck in AI:
moving data from memory to compute.

Below is a clear, executive-level breakdown of what CIM/PIM really are, why they matter, and where the technology stands today.

CIM — Compute Inside Memory Cells

Compute is done within the memory array itself (often analog).
Example:

  • SRAM/DRAM/Flash cell performs a partial MAC operation

  • Use Ohm’s law + Kirchhoff’s law to perform vector-matrix operations

This is extremely energy-efficient and parallel.

CIM = full fusion of compute + memory.

PIM — Compute Near Memory

Compute is done next to memory using small accelerator blocks.

Examples:

  • DRAM with integrated ALUs

  • HBM stack with logic layer (HBM-PIM)

  • Samsung’s AXDIMM, HBM-PIM

  • Near-memory FPGA tiles

PIM = memory with local compute to reduce data movement.

 

Commercial / Near-Commercial Leaders

Samsung

  • AXDIMM PIM DDR4/5

  • HBM-PIM stacked DRAM with logic layer
    Best positioned for mainstream adoption.

SK Hynix

  • HBM3E PIM prototypes

  • Dataflow-style logic layers

Intel / Micron

  • Exploring NDP (near-data processing)

  • Not as far along as Samsung.

Startups (Most Innovative)

Mythic AI

  • Analog compute-in-memory (flash-based) for edge inference

  • Excellent efficiency, accuracy challenges

Rain AI

  • Next-gen analog CIM tile arrays

  • Model weights stored directly in analog arrays

  • Very promising for low-power LLM inference

MemryX

  • Near-memory compute for edge AI

  • Simplified dataflow architecture

Cerebras (partially CIM-like)

  • Wafer-scale engine with distributed local memory

  • Not CIM, but memory-centric and post-GPU

 

 

 

 




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