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arXiv · 2405.04326

A Calibratable Model for Fast Energy Estimation of MVM Operations on RRAM Crossbars

José Cubero-Cascante·Arunkumar Vaidyanathan·Rebecca Pelke·Lorenzo Pfeifer·Rainer Leupers·Jan Moritz Joseph
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Abstract

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%.

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José Cubero-Cascante, Arunkumar Vaidyanathan, Rebecca Pelke, Lorenzo Pfeifer, Rainer Leupers, Jan Moritz Joseph. 2024-05-07. A Calibratable Model for Fast Energy Estimation of MVM Operations on RRAM Crossbars. https://arxiv.org/abs/2405.04326

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