XBTorch统一模拟存内计算加速器，支持硬件协同设计与容错。
XBTorch: A Unified Framework for Modeling and Co-Design of Crossbar-Based Deep Learning Accelerators
- 基于新兴存储技术构建交叉阵列模型，集成于PyTorch
- 支持设备级建模、软硬件协同设计与推理容错
- 可适配FeFET、ReRAM等技术，支持自定义器件模型
新兴存储技术因其可在内存中直接进行计算，成为突破传统冯·诺依曼架构在深度学习应用中瓶颈的有前途路径。这些基于纳米级器件、具备可调且非易失性电导特性的技术，相比传统架构能显著降低能耗和延迟。本文提出XBTorch（CrossBarTorch），一个与PyTorch无缝集成的仿真框架，提供针对基于新兴存储技术的交叉阵列系统进行精确高效建模的专用工具。通过硬件感知训练与推理的详细对比及案例研究，我们展示了XBTorch在设备级建模、跨层协同设计和推理时故障容错等关键研究领域提供的统一接口。虽然示例研究采用铁电场效应晶体管（FeFET）模型，但该框架保持技术无关性——支持其他新兴存储器如电阻式随机存取存储器（ReRAM），并允许用户定义自定义器件模型。代码已公开：https://github.com/ADAM-Lab-GW/xbtorch
原文摘要 · Abstract (English)
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
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。