Optimizing Binary and Ternary Neural Network Inference on RRAM Crossbars using CIM-Explorer

• URL: http://arxiv.org/abs/2505.14303v1
• Date: Tue, 20 May 2025 12:54:48 GMT
• Title: Optimizing Binary and Ternary Neural Network Inference on RRAM Crossbars using CIM-Explorer
• Authors: Rebecca Pelke, José Cubero-Cascante, Nils Bosbach, Niklas Degener, Florian Idrizi, Lennart M. Reimann, Jan Moritz Joseph, Rainer Leupers,
• Abstract summary: We introduce CIM-Explorer, a modular toolkit for optimizing BNN and TNN inference on RRAM crossbars.<n> CIM-Explorer includes an end-to-end compiler stack, multiple mapping options, and simulators.<n>We demonstrate the expected accuracy for various mappings and crossbar parameters.
• Score: 0.1505692475853115
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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