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      OptMem: dark-silicon aware low latency hybrid memory design

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      Author
      Onsori, Salman
      Asad, Arghavan A
      Raahemifar, K.
      Fathy, M.
      Date
      2016-01
      Source Title
      2016 International Conference on VLSI Systems, Architectures, Technology and Applications, VLSI-SATA 2016
      Publisher
      IEEE
      Language
      English
      Type
      Conference Paper
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      Abstract
      In this article, we present a convex optimization model to design a three dimension (3D)stacked hybrid memory system to improve performance in the dark silicon era. Our convex model optimizes numbers and placement of static random access memory (SRAM) and spin-Transfer torque magnetic random-Access memory(STT-RAM) memories on the memory layer to exploit advantages of both technologies. Power consumption that is the main challenge in the dark silicon era is represented as a main constraint in this work and it is satisfied by the detailed optimization model in order to design a dark silicon aware 3D Chip-Multiprocessor (CMP). Experimental results show that the proposed architecture improves the energy consumption and performanceof the 3D CMPabout 25.8% and 12.9% on averagecompared to the Baseline memory design. © 2016 IEEE.
      Keywords
      3D integration
      Convex optimization
      Dark silicon
      Embedded chip-multiprocessor (eCMP)
      Hybrid memory architecture
      Non-volatile memory (NVM)
      Convex optimization
      Data storage equipment
      Digital storage
      Energy utilization
      Integrated circuit design
      Magnetic storage
      Memory architecture
      Multiprocessing systems
      Optimization
      Silicon
      Static random access storage
      VLSI circuits
      3-D integration
      Dark silicons
      Embedded chips
      Non-volatile memory
      Uncore components
      Random access storage
      Permalink
      http://hdl.handle.net/11693/37686
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/VLSI-SATA.2016.7593059
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      • Department of Computer Engineering 1368
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