TY - CONF T1 - NVMExplorer: A Framework for Cross-Stack Comparisons of Embedded Non-Volatile Memories T2 - 2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA) Y1 - 2022 A1 - L. Pentecost A1 - A. Hankin A1 - M. Donato A1 - M. Hempstead A1 - G.-Y. Wei A1 - D. Brooks AB - 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. JF - 2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA) CY - Seoul, South Korea UR - https://doi.org/10.48550/arXiv.2109.01188 ER -