论文标题
深度复发神经网络的Litelstm体系结构
LiteLSTM Architecture for Deep Recurrent Neural Networks
论文作者
论文摘要
长期记忆（LSTM）是用于学习时空顺序数据的强大复发性神经网络体系结构。但是，它需要从软件和硬件方面进行学习和实施的重要计算能力。本文提出了一种新颖的LITELSTM架构，基于使用权重共享概念减少LSTM的计算组件，以降低整体体系结构成本并保持体系结构的性能。拟议的LITELSTM对于学习时间消耗至关重要的大数据（例如IoT设备和医疗数据的安全性）可能很重要。此外，它有助于减少二氧化碳的足迹。从计算机视觉和网络安全域中对两个不同数据集进行了经验评估和测试所提出的模型。
Long short-term memory (LSTM) is a robust recurrent neural network architecture for learning spatiotemporal sequential data. However, it requires significant computational power for learning and implementing from both software and hardware aspects. This paper proposes a novel LiteLSTM architecture based on reducing the computation components of the LSTM using the weights sharing concept to reduce the overall architecture cost and maintain the architecture performance. The proposed LiteLSTM can be significant for learning big data where time-consumption is crucial such as the security of IoT devices and medical data. Moreover, it helps to reduce the CO2 footprint. The proposed model was evaluated and tested empirically on two different datasets from computer vision and cybersecurity domains.
