  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.
