使用非易失性突触模拟模式识别：MRAM、铁电体与磁性斯格明子
Simulating Pattern Recognition Using Non-volatile Synapses: MRAM, Ferroelectrics and Magnetic Skyrmions
AI总结：
该研究构建仿真环境，系统比较MRAM、铁电FET和斯格明子纳米轨道等非易失性自旋电子突触在图像与声音分类神经网络中的性能，以支撑低功耗神经形态硬件设计。
摘要：
本项目通过一种全面的基于仿真的方法，探索非易失性突触在神经形态计算中用于模式识别任务。主要途径是自旋电子突触，它利用电子的自旋特性实现高效的数据处理与存储。这为传统电子突触提供了一种有前景的替代方案，因为传统电子突触需要持续供电以防止数据泄漏。目标是开发并仿真一个融合自旋电子突触的神经网络模型，考察其执行图像和声音分类等复杂模式识别任务的潜力。通过构建仿真环境，我们将复现多种模型，包括基于自旋转移矩的MRAM、基于压控磁各向异性的MRAM、铁电场效应晶体管，以及用于突触器件的基于斯格明子的纳米轨道，以评估它们的性能并比较不同非易失性实现方式的结果。研究结果将凸显自旋电子突触在构建低功耗、高性能神经形态硬件方面的有效性，并为其在未来节能人工智能系统中的应用提供宝贵洞见。
英文摘要：
This project explores the use of non-volatile synapses in neuromorphic computing for pattern recognition tasks through a comprehensive simulation-based approach. The main approach is through spintronic synapses, which leverage the electron's spin properties to achieve efficient data processing and storage. This offers a promising alternative to traditional electronic synapses which require constant power recharge to prevent data leakage. The goal is to develop and simulate a neural network model that incorporates spintronic synapses, examining their potential to perform complex pattern recognition tasks such as image and sound classification. By building a simulation environment, we will replicate various models, including spin transfer torque based MRAM, voltage controlled magnetic anisotropy based MRAM, ferroelectric field effect transistors, and skyrmion based nanotrack for synaptic devices, to evaluate their performance and compare results across different non-volatile implementations. The findings will highlight the effectiveness of spintronic synapses in creating low-power, high-performance neuromorphic hardware, providing valuable insights into their application for future energy-efficient artificial intelligence systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
