NVMExplorer: A Framework for Cross-Stack Comparisons of Embedded Non-Volatile Memories


L. Pentecost, A. Hankin, M. Donato, M. Hempstead, G.-Y. Wei, and D. Brooks. 4/2/2022. “NVMExplorer: A Framework for Cross-Stack Comparisons of Embedded Non-Volatile Memories.” In 2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA). Seoul, South Korea. Publisher's Version
2109.01188.pdf1.85 MB


Repeated off-chip memory accesses to DRAM drive up operating power for data-intensive applications, and SRAM technology scaling and leakage power limits the efficiency of embedded memories. Future on-chip storage will need higher density and energy efficiency, and the actively expanding field of emerging, embeddable non-volatile memory (eNVM) technologies is providing many potential candidates to satisfy this need. Each technology proposal presents distinct trade-offs in terms of density, read, write, and reliability characteristics, and we present a comprehensive framework for navigating and quantifying these design trade-offs alongside realistic system constraints and application-level impacts. This work evaluates eNVM-based storage for a range of application and system contexts including machine learning on the edge, graph analytics, and general purpose cache hierarchy, in addition to describing a freely available (this http URL) set of tools for application experts, system designers, and device experts to better understand, compare, and quantify the next generation of embedded memory solutions.
See also: eNVM
Last updated on 05/05/2022