Optimizing Binary and Ternary Neural Network Inference on RRAM Crossbars using CIM-Explorer
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911523782262784 |
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| author | Pelke, Rebecca Cubero-Cascante, José Bosbach, Nils Degener, Niklas Idrizi, Florian Reimann, Lennart M. Joseph, Jan Moritz Leupers, Rainer |
| author_facet | Pelke, Rebecca Cubero-Cascante, José Bosbach, Nils Degener, Niklas Idrizi, Florian Reimann, Lennart M. Joseph, Jan Moritz Leupers, Rainer |
| contents | Using Resistive Random Access Memory (RRAM) crossbars in Computing-in-Memory (CIM) architectures offers a promising solution to overcome the von Neumann bottleneck. Due to non-idealities like cell variability, RRAM crossbars are often operated in binary mode, utilizing only two states: Low Resistive State (LRS) and High Resistive State (HRS). Binary Neural Networks (BNNs) and Ternary Neural Networks (TNNs) are well-suited for this hardware due to their efficient mapping. Existing software projects for RRAM-based CIM typically focus on only one aspect: compilation, simulation, or Design Space Exploration (DSE). Moreover, they often rely on classical 8 bit quantization. To address these limitations, we introduce CIM-Explorer, a modular toolkit for optimizing BNN and TNN inference on RRAM crossbars. CIM-Explorer includes an end-to-end compiler stack, multiple mapping options, and simulators, enabling a DSE flow for accuracy estimation across different crossbar parameters and mappings. CIM-Explorer can accompany the entire design process, from early accuracy estimation for specific crossbar parameters, to selecting an appropriate mapping, and compiling BNNs and TNNs for a finalized crossbar chip. In DSE case studies, we demonstrate the expected accuracy for various mappings and crossbar parameters. CIM-Explorer can be found on GitHub. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_14303 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimizing Binary and Ternary Neural Network Inference on RRAM Crossbars using CIM-Explorer Pelke, Rebecca Cubero-Cascante, José Bosbach, Nils Degener, Niklas Idrizi, Florian Reimann, Lennart M. Joseph, Jan Moritz Leupers, Rainer Emerging Technologies Machine Learning Using Resistive Random Access Memory (RRAM) crossbars in Computing-in-Memory (CIM) architectures offers a promising solution to overcome the von Neumann bottleneck. Due to non-idealities like cell variability, RRAM crossbars are often operated in binary mode, utilizing only two states: Low Resistive State (LRS) and High Resistive State (HRS). Binary Neural Networks (BNNs) and Ternary Neural Networks (TNNs) are well-suited for this hardware due to their efficient mapping. Existing software projects for RRAM-based CIM typically focus on only one aspect: compilation, simulation, or Design Space Exploration (DSE). Moreover, they often rely on classical 8 bit quantization. To address these limitations, we introduce CIM-Explorer, a modular toolkit for optimizing BNN and TNN inference on RRAM crossbars. CIM-Explorer includes an end-to-end compiler stack, multiple mapping options, and simulators, enabling a DSE flow for accuracy estimation across different crossbar parameters and mappings. CIM-Explorer can accompany the entire design process, from early accuracy estimation for specific crossbar parameters, to selecting an appropriate mapping, and compiling BNNs and TNNs for a finalized crossbar chip. In DSE case studies, we demonstrate the expected accuracy for various mappings and crossbar parameters. CIM-Explorer can be found on GitHub. |
| title | Optimizing Binary and Ternary Neural Network Inference on RRAM Crossbars using CIM-Explorer |
| topic | Emerging Technologies Machine Learning |
| url | https://arxiv.org/abs/2505.14303 |