Memristors, emerging non-volatile memory devices, have shown promising potential in neuromorphic hardware designs, especially in spiking neural network (SNN) hardware implementation. Memristor-based SNNs have been successfully applied in a wide range of various applications, including image classification and pattern recognition. However, implementing memristor-based SNNs in text classification is still under exploration. One of the main reasons is that training memristor-based SNNs for text classification is costly due to the lack of efficient learning rules and memristor non-idealities. To address these issues and accelerate the research of exploring memristor-based spiking neural networks in text classification applications, we develop a simulation framework with a virtual memristor array using an empirical memristor model. We use this framework to demonstrate a sentiment analysis task in the IMDB movie reviews dataset. We take two approaches to obtain trained spiking neural networks with memristor models: 1) by converting a pre-trained artificial neural network (ANN) to a memristor-based SNN, or 2) by training a memristor-based SNN directly. These two approaches can be applied in two scenarios: offline classification and online training. We achieve the classification accuracy of 85.88% by converting a pre-trained ANN to a memristor-based SNN and 84.86% by training the memristor-based SNN directly, given that the baseline training accuracy of the equivalent ANN is 86.02%. We conclude that it is possible to achieve similar classification accuracy in simulation from ANNs to SNNs and from non-memristive synapses to data-driven memristive synapses. We also investigate how global parameters such as spike train length, the read noise, and the weight updating stop conditions affect the neural networks in both approaches.
翻译:正在探索在文本分类中实施基于分子的 SNN 的 SNN 。 其中一个主要原因是对基于分子的 SNN 进行文本分类培训的成本很高, 原因是缺乏高效学习的参数和内存的非理想。 为了解决这些问题并加速在文本分类应用程序中探索基于分子的线性分类网络。 我们利用一个模拟框架在IMDB 电影审查数据集中展示情绪分析任务。 我们采取两种办法, 利用基于分子的模型获得经过训练的智能网络。 我们通过将一个经过训练的自然神经网络(ANN) 转换成一个基于精度的 SNNNP 的精度分类网络(ANNP ), 加速在文本分类应用程序中探索基于分子的线性分类系统网络。 我们开发一个模拟框架,使用一个虚拟模擬模擬的模擬阵列阵列阵列阵列阵列, 并且通过SNNNO 的S- mal IM 直接将S- main 的SNNO 模型转换成一个不以内。