@conference {1435471,
	title = {NVMExplorer: A Framework for Cross-Stack Comparisons of Embedded Non-Volatile Memories},
	booktitle = {2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA)},
	year = {2022},
	address = {Seoul, South Korea},
	abstract = {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.},
	url = {https://doi.org/10.48550/arXiv.2109.01188},
	author = {L. Pentecost and A. Hankin and M. Donato and M. Hempstead and G.-Y. Wei and D. Brooks}
}
