dorsal/arxiv
View SchemaXBTorch: 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 |
|---|---|
| Categories | |
| ArXiv ID | 2601.07086vv1 |
| URL | https://arxiv.org/abs/2601.07086 |
| 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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"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",
"arxiv_id": "2601.07086",
"authors": [
"Osama Yousuf",
"Andreu L. Glasmann",
"Martin Lueker-Boden",
"Sina Najmaei",
"Gina C. Adam"
],
"categories": [
"cs.ET",
"cs.AI",
"cs.LG"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "XBTorch: A Unified Framework for Modeling and Co-Design of Crossbar-Based Deep Learning Accelerators",
"url": "https://arxiv.org/abs/2601.07086",
"version": "v1"
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