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Management and Optimization for Nonvolatile Memory-Based Hybrid Scratchpad Memory on Multicore Embedded Processors

  • Jingtong HU
  • , Qingfeng ZHUGE
  • , Chun Jason XUE
  • , Wei-Che TSENG
  • , Edwin H.-M. SHA

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

The recent emergence of various Non-Volatile Memories (NVMs), with many attractive characteristics such as low leakage power and high-density, provides us with a new way of addressing the memory power consumption problem. In this article, we target embedded CMPs, and propose a novel Hybrid Scratch Pad Memory (HSPM) architecture which consists of SRAM and NVM to take advantage of the ultra-low leakage power, high density of NVM, and fast access of SRAM. A novel data allocation algorithm as well as an algorithm to determine the NVM/SRAM ratio for the novel HSPM architecture are proposed. The experimental results show that the data allocation algorithm can reduce the memory access time by 33.51% and the dynamic energy consumption by 16.81% on average for the HSPM architecture when compared with a greedy algorithm. The NVM/SRAM size determination algorithm can further reduce the memory access time by 14.7% and energy consumption by 20.1% on average. © 2014 ACM
Original languageEnglish
Article number79
JournalACM Transactions on Embedded Computing Systems
Volume13
Issue number4
Online published10 Mar 2014
DOIs
Publication statusPublished - 5 Dec 2014

Funding

This work is partially supported by NSF CNS-1015802, Texas NHARP 009741-0020-2009, NSFC 61173014, NSFC 61133005, and grants from the Research Grants Council of the Hong Kong Special Administrative Region, China [Project No. CityU 123811, 123210].

Research Keywords

  • Data allocation
  • Energy
  • MRAM
  • Multicore processors
  • NVM
  • On-chip memory
  • PCM
  • SPM

RGC Funding Information

  • RGC-funded

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