Management and optimization for nonvolatile memory-based hybrid scratchpad memory on multicore embedded processors

  • Authors:
  • Jingtong Hu;Qingfeng Zhuge;Chun Jason Xue;Wei-Che Tseng;Edwin H.-M. Sha

  • Affiliations:
  • University of Texas at Dallas, Stillwater, OK;Chongqing University, Chongqing, China;City University of Hong Kong, Kowloon, Hong Kong;University of Texas at Dallas, Richardson, TX;Chongqing University and University of Texas at Dallas, Chongqing, China and Richardson, TX

  • Venue:
  • ACM Transactions on Embedded Computing Systems (TECS)
  • Year:
  • 2014

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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.