  This is part III of three-part work. In parts I and II, we have presented
eight variants for simplified Long Short Term Memory (LSTM) recurrent neural
networks (RNNs). It is noted that fast computation, specially in constrained
computing resources, are an important factor in processing big time-sequence
data. In this part III paper, we present and evaluate two new LSTM model
variants which dramatically reduce the computational load while retaining
comparable performance to the base (standard) LSTM RNNs. In these new variants,
we impose (Hadamard) pointwise state multiplications in the cell-memory network
in addition to the gating signal networks.
