High-throughput PIM (Processing in-Memory) for DRAM using Bank-level Pipelined Architecture

  • Lee, Hyunsoo; 
  • Lee, Hyundong; 
  • Shin, Minseung; 
  • Shin, Gyuri; 
  • Jeon, Sumin; 
  • ... Song, Taigon
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초록

Artificial intelligence (AI) is a technology requires massive computation. Among many solutions that accelerate AI for faster computation and low power, processing-in-memory (PIM) is a promising candidate. In this paper, we propose a PIM architecture of DRAM via custom pipelining. Our architecture proposes pipelining of operation units that leads to massive throughput, where an operation unit consists of eight banks. Our optimized pipelined architecture shows a -19.16% reduction in power-delay-product (PDP) per area and 24.1% better throughput compared to the latest DRAM PIM architecture with only 0.7% area overhead.

키워드

processing in-memory (PIM); Pipelining; DRAM
제목
High-throughput PIM (Processing in-Memory) for DRAM using Bank-level Pipelined Architecture
저자
Lee, Hyunsoo; Lee, Hyundong; Shin, Minseung; Shin, Gyuri; Jeon, Sumin; Song, Taigon
DOI
10.1109/ISOCC59558.2023.10396302
발행일
2023
유형
Proceedings Paper
저널명
2023 20TH INTERNATIONAL SOC DESIGN CONFERENCE, ISOCC
페이지
101 ~ 102