mlsanityData to 14 Oct 2026

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

arXiv 2505.14303 ↗First posted 20 May 2025cs.ET cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 10 of its 44 references that are in the map. First posted on arXiv 20 May 2025.

Builds on

10 of 44 references.

References · 10

  1. 2016Ternary Weight Networkscited by 204
  2. 2020Binary Neural Networks: A Surveycited by 190
  3. 2021A flexible and fast PyTorch toolkit for simulating training and inference on analog crossbar arrayscited by 39
  4. 2021On the Accuracy of Analog Neural Network Inference Acceleratorscited by 10
  5. 2022Neural-PIM: Efficient Processing-In-Memory with Neural Approximation of Peripheralscited by 5
  6. 2022CINM (Cinnamon): A Compilation Infrastructure for Heterogeneous Compute In-Memory and Compute Near-Memory Paradigmscited by 5
  7. 2023Mapping of CNNs on multi-core RRAM-based CIM architecturescited by 4
  8. 2018Neural Network-Hardware Co-design for Scalable RRAM-based BNN Acceleratorscited by 3
  9. 2020TDO-CIM: Transparent Detection and Offloading for Computation In-memorycited by 3
  10. 2025Evaluating the Scalability of Binary and Ternary CNN Workloads on RRAM-based Compute-in-Memory Acceleratorscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Mixed-Precision Training and Compilation for RRAM-based Computing-in-Memory Acceleratorscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2024BinSparX: Sparsified Binary Neural Networks for Reduced Hardware Non-Idealities in Xbar Arrays
  2. 2025CIMNAS: A Joint Framework for Compute-In-Memory-Aware Neural Architecture Search
  3. 2023Comprehensive Benchmarking of Binary Neural Networks on NVM Crossbar Architectures
  4. 2021PR-CIM: a Variation-Aware Binary-Neural-Network Framework for Process-Resilient Computation-in-memory
  5. 2026Multibit neural inference in a N-ary crossbar architecture
  6. 2025CIMPool: Scalable Neural Network Acceleration for Compute-In-Memory using Weight Pools
  7. 2023Examining the Role and Limits of Batchnorm Optimization to Mitigate Diverse Hardware-noise in In-memory Computing
  8. 2022Design Space Exploration of Dense and Sparse Mapping Schemes for RRAM Architectures