XBTorch: A Unified Framework for Modeling and Co-Design of Crossbar-Based Deep Learning Accelerators

• URL: http://arxiv.org/abs/2601.07086v1
• Date: Sun, 11 Jan 2026 22:35:30 GMT
• Title: XBTorch: A Unified Framework for Modeling and Co-Design of Crossbar-Based Deep Learning Accelerators
• Authors: Osama Yousuf, Andreu L. Glasmann, Martin Lueker-Boden, Sina Najmaei, Gina C. Adam,
• Abstract summary: This paper introduces XBTorch, a novel simulation framework that integrates seamlessly with PyTorch.<n>XBTorch provides specialized tools for accurately and efficiently modeling crossbar-based systems based on emerging memory technologies.
• Score: 0.5834731599084116
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Emerging memory technologies have gained significant attention as a promising pathway to overcome the limitations of conventional computing architectures in deep learning applications. By enabling computation directly within memory, these technologies - built on nanoscale devices with tunable and nonvolatile conductance - offer the potential to drastically reduce energy consumption and latency compared to traditional von Neumann systems. This paper introduces XBTorch (short for CrossBarTorch), a novel simulation framework that integrates seamlessly with PyTorch and provides specialized tools for accurately and efficiently modeling crossbar-based systems based on emerging memory technologies. Through detailed comparisons and case studies involving hardware-aware training and inference, we demonstrate how XBTorch offers a unified interface for key research areas such as device-level modeling, cross-layer co-design, and inference-time fault tolerance. While exemplar studies utilize ferroelectric field-effect transistor (FeFET) models, the framework remains technology-agnostic - supporting other emerging memories such as resistive RAM (ReRAM), as well as enabling user-defined custom device models. The code is publicly available at: https://github.com/ADAM-Lab-GW/xbtorch

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