Networked Power-Gated MRAMs for Memory-Based Computing

Jean Philippe Diguet, Naoya Onizawa, Mostafa Rizk, Johanna Sepulveda, Amer Baghdadi, Takahiro Hanyu

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Emerging nonvolatile memory technologies open new perspectives for original computing architectures. In this paper, we propose a new type of flexible and energy-efficient architecture that relies on power-gated distributed magnetoresistive random access memory (MRAM). The proposed architecture uses a network-on-chip (NoC) to interconnect MRAM-based clusters, processing elements, and managers. The NoC distributes application-specific commands to MRAM devices by means of packets. Configurable network interfaces allow to transform MRAM devices into smart units able to respond to incoming commands. In this context, three types of MRAM designs are proposed with different power-gating policies and granularities. A relevant database search engine case study is considered to illustrate the benefits of this proposed architecture. It is implemented with a sparse-neural-network approach and simulated in SystemC with different scenarios including hundreds of database queries. Hardware designs and accurate power estimations have been conducted. The obtained results demonstrate important power reduction with database hit rates of about 94%. Targeting 65-nm technology, energy savings reach 87% when compared with an static random access memory-based implementation. Moreover, a new asymmetric read/write MRAM type provides from 39% to 50% energy reduction with respect to the other fixed-granularity models. This results in a low-power, highly scalable, and configurable implementation of memory-based computing.

Original languageEnglish
Article number8428653
Pages (from-to)2696-2708
Number of pages13
JournalIEEE Transactions on Very Large Scale Integration (VLSI) Systems
Issue number12
Publication statusPublished - 2018 Dec


  • Database
  • magnetoresistive random access memory (MRAM)
  • network-on-chip (NoC)
  • power gating (PG)
  • sparse neural networks (SNNs)

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Electrical and Electronic Engineering


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