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.

该研究论文介绍了一种基于权重共享概念的新型 LiteLSTM 体系结构，通过减少 LSTM 的计算组件来降低整体架构成本和维护架构性能，从而提高学习大数据的效率，尤其适用于物联网设备和医学数据的安全性，同时有助于减少 CO2 排放。该模型在计算机视觉和网络安全领域的两个不同数据集上进行了实证评估和测试。