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    Design and Evaluation of Monolithic Computers Implemented Using Crossbar ReRAM

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    UMIACS-TR-2019-01.pdf (1.152Mb)
    No. of downloads: 150

    Date
    2019-07-16
    Author
    Jagasivamani, Meenatchi
    Walden, Candace
    Singh, Devesh
    Li, Shang
    Kang, Luyi
    Asnaashari, Mehdi
    Dubois, Sylvain
    Jacob, Bruce
    Yeung, Donald
    DRUM DOI
    https://doi.org/10.13016/uzkp-whtx
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    Abstract
    A monolithic computer is an emerging architecture in which a multicore CPU and a high-capacity main memory system are all integrated in a single die. We believe such architectures will be possible in the near future due to nonvolatile memory technology, such as the resistive random access memory, or ReRAM, from Crossbar Incorporated. Crossbar's ReRAM can be fabricated in a standard CMOS logic process, allowing it to be integrated into a CPU's die. The ReRAM cells are manufactured in between metal wires and do not employ per-cell access transistors, leaving the bulk of the base silicon area vacant. This means that a CPU can be monolithically integrated directly underneath the ReRAM memory, allowing the cores to have massively parallel access to the main memory. This paper presents the characteristics of Crossbar's ReRAM technology, informing architects on how ReRAM can enable monolithic computers. Then, it develops a CPU and memory system architecture around those characteristics, especially to exploit the unprecedented memory-level parallelism. The architecture employs a tiled CPU, and incorporates memory controllers into every compute tile that support a variable access granularity to enable high scalability. Lastly, the paper conducts an experimental evaluation of monolithic computers on graph kernels and streaming computations. Our results show that compared to a DRAM-based tiled CPU, a monolithic computer achieves 4.7x higher performance on the graph kernels, and achieves roughly parity on the streaming computations. Given a future 7nm technology node, a monolithic computer could outperform the conventional system by 66% for the streaming computations.
    URI
    http://hdl.handle.net/1903/22243
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    DRUM is brought to you by the University of Maryland Libraries
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