A Calibratable Model for Fast Energy Estimation of MVM Operations on RRAM Crossbars
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917663874220032 |
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| author | Cubero-Cascante, José Vaidyanathan, Arunkumar Pelke, Rebecca Pfeifer, Lorenzo Leupers, Rainer Joseph, Jan Moritz |
| author_facet | Cubero-Cascante, José Vaidyanathan, Arunkumar Pelke, Rebecca Pfeifer, Lorenzo Leupers, Rainer Joseph, Jan Moritz |
| contents | 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%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_04326 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Calibratable Model for Fast Energy Estimation of MVM Operations on RRAM Crossbars Cubero-Cascante, José Vaidyanathan, Arunkumar Pelke, Rebecca Pfeifer, Lorenzo Leupers, Rainer Joseph, Jan Moritz Signal Processing C.3; I.2; I.6 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%. |
| title | A Calibratable Model for Fast Energy Estimation of MVM Operations on RRAM Crossbars |
| topic | Signal Processing C.3; I.2; I.6 |
| url | https://arxiv.org/abs/2405.04326 |