用于深度学习应用的晶体管交叉阵列仿真

IF 2.1 4区工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Nanotechnology Pub Date : 2024-06-17 DOI:10.1109/TNANO.2024.3415382

Elvis Díaz Machado;Jose Lopez Vicario;Enrique Miranda;Antoni Morell

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引用次数: 0

摘要

基于交叉棒阵列的硬件神经网络（HNN）由于特征尺寸小，有望成为解决复杂任务的高能效计算架构。虽然已有软件库可以处理忆阻器网络的电路仿真，但它们仍然超出了任何消费级 GPU 的 VRAM 内存，无法用于大规模的交叉条阵列，同时计算复杂度也很高。这项研究讨论了一种迭代方法，它能在更有限的内存使用范围内对相应的忆阻器交叉阵列进行快速仿真。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Memristor Crossbar Array Simulation for Deep Learning Applications

Hardware neural networks (HNNs) based on crossbar arrays are expected to be energy-efficient computing architectures for solving complex tasks due to their small feature sizes. Although there exist software libraries able to deal with circuit simulation of memristor networks, they still exceed the memory available of any consumer grade GPU's VRAM for large scale crossbar arrays while having a significant computational complexity. This work discusses an iterative method to implement a fast simulation of the corresponding memristor crossbar array with much more limited memory use.

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来源期刊

IEEE Transactions on Nanotechnology 工程技术-材料科学：综合

CiteScore

4.80

自引率

8.30%

发文量

审稿时长

8.3 months

期刊介绍： The IEEE Transactions on Nanotechnology is devoted to the publication of manuscripts of archival value in the general area of nanotechnology, which is rapidly emerging as one of the fastest growing and most promising new technological developments for the next generation and beyond.