  Long short-term memory (LSTM) is one of the robust recurrent neural network
architectures for learning sequential data. However, it requires considerable
computational power to learn and implement both software and hardware aspects.
This paper proposed a novel LiteLSTM architecture based on reducing the LSTM
computation components via the weights sharing concept to reduce the overall
architecture computation cost and maintain the architecture performance. The
proposed LiteLSTM can be significant for processing large data where
time-consuming is crucial while hardware resources are limited, such as the
security of IoT devices and medical data processing. The proposed model was
evaluated and tested empirically on three different datasets from the computer
vision, cybersecurity, speech emotion recognition domains. The proposed
LiteLSTM has comparable accuracy to the other state-of-the-art recurrent
architecture while using a smaller computation budget.
