The surge in AI usage demands innovative power reduction strategies. Novel Compute-in-Memory (CIM) architectures, leveraging advanced memory technologies, hold the potential for significantly lowering energy consumption by integrating storage with parallel Matrix-Vector-Multiplications (MVMs). This study addresses the 1T1R RRAM crossbar, a core component in numerous CIM architectures. We introduce an abstract model and a calibration methodology for estimating operational energy. Our tool condenses circuit-level behaviour into a few parameters, facilitating energy assessments for DNN workloads. Validation against low-level SPICE simulations demonstrates speedups of up to 1000x and energy estimations with errors below 1%.
@article{arxiv.2405.04326,
title = {A Calibratable Model for Fast Energy Estimation of MVM Operations on RRAM Crossbars},
author = {José Cubero-Cascante and Arunkumar Vaidyanathan and Rebecca Pelke and Lorenzo Pfeifer and Rainer Leupers and Jan Moritz Joseph},
journal= {arXiv preprint arXiv:2405.04326},
year = {2024}
}
Comments
Pre-print of work presented at AICAS 2024. 5 pages, 6 figures