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

arXiv: https://arxiv.org/abs/2601.07086 · HTML: https://mlsanity.com/paper/2601.07086/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 38 references that are in the map.
- First posted on arXiv 11 Jan 2026.

## Builds on (most cited first, 14 of 14)

- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [PIQA: Reasoning about Physical Commonsense in Natural Language](https://mlsanity.com/paper/1911.11641/) (2019)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions](https://mlsanity.com/paper/1905.10044/) (2019)
- [Visualizing the Loss Landscape of Neural Nets](https://mlsanity.com/paper/1712.09913/) (2017)
- [PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization](https://mlsanity.com/paper/1905.13727/) (2019)
- [Training and Inference with Integers in Deep Neural Networks](https://mlsanity.com/paper/1802.04680/) (2018)
- [A flexible and fast PyTorch toolkit for simulating training and inference on analog crossbar arrays](https://mlsanity.com/paper/2104.02184/) (2021)
- [QPyTorch: A Low-Precision Arithmetic Simulation Framework](https://mlsanity.com/paper/1910.04540/) (2019)
- [Spectra: Surprising Effectiveness of Pretraining Ternary Language Models at Scale](https://mlsanity.com/paper/2407.12327/) (2024)
- [Algorithm for Training Neural Networks on Resistive Device Arrays](https://mlsanity.com/paper/1909.07908/) (2019)
- [Streaming Batch Eigenupdates for Hardware Neuromorphic Networks](https://mlsanity.com/paper/1903.01635/) (2019)
- [Measurement-driven neural-network training for integrated magnetic tunnel junction arrays](https://mlsanity.com/paper/2312.06446/) (2023)
- [Device Modeling Bias in ReRAM-based Neural Network Simulations](https://mlsanity.com/paper/2211.15925/) (2022)

## Built on by (most cited first, 1 of 1)

- [RACE-AIMC: Selective Inference for Heterogeneous Analog In-Memory Accelerators at the Edge](https://mlsanity.com/paper/2609.03149/) (2026)

## Abstract (arXiv)

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