Achieving the Performance of All-Bank In-DRAM PIM With Standard Memory Interface: Memory-Computation Decoupling

Yoonah Paik, Chang Hyun Kim, Won Jun Lee, Seon Wook Kim

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


Processing-in-Memory (PIM) has been actively studied to overcome the memory bottleneck by placing computing units near or in memory, especially for efficiently processing low locality data-intensive applications. We can categorize the in-DRAM PIMs depending on how many banks perform the PIM computation by one DRAM command: per-bank and all-bank. The per-bank PIM operates only one bank, delivering low performance but preserving the standard DRAM interface and servicing non-PIM requests during PIM execution. The all-bank PIM operates all banks, achieving high performance but accompanying design issues like thermal and power consumption. We introduce the memory-computation decoupling execution to achieve the ideal all-bank PIM performance while preserving the standard JEDEC DRAM interface, i.e., performing the per-bank execution, thus easily adapted to commercial platforms. We divide the PIM execution into two phases: memory and computation phases. At the memory phase, we read the bank-private operands from a bank and store them in PIM engines' registers bank-by-bank. At the computation phase, we decouple the PIM engine from the memory array and broadcast a bank-shared operand using a standard read/write command to make all banks perform the computation simultaneously, thus reaching the computing throughput of the all-bank PIM. For extending the computation phase, i.e., maximizing all-bank execution opportunity, we introduce a compiler analysis and code generation technique to identify the bank-private and the bank-shared operands. We compared the performance of Level-2/3 BLAS, multi-batch LSTM-based Seq2Seq model, and BERT on our decoupled PIM with commercial computing platforms. In Level-3 BLAS, we achieved speedups of 75.8times , 1.2times , and 4.7times compared to CPU, GPU, and the per-bank PIM and up to 91.4% of the ideal all-bank PIM performance. Furthermore, our decoupled PIM consumed less energy than GPU and the per-bank PIM by 72.0% and 78.4%, but 7.4%, a little more than the ideal all-bank PIM.

Original languageEnglish
Pages (from-to)93256-93272
Number of pages17
JournalIEEE Access
Publication statusPublished - 2022

Bibliographical note

Publisher Copyright:
© 2013 IEEE.


  • all-bank execution
  • in-memory processing
  • Memory-computation decoupling
  • standard memory interface

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering
  • Electrical and Electronic Engineering


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