Shallow RNN: Accurate Time-series Classification on Resource Constrained Devices

Recurrent Neural Networks (RNNs) capture long dependencies and context, and hence are the key component of typical sequential data based tasks. However, the sequential nature of RNNs dictates a large inference cost for long sequences even if the hardware supports parallelization. To induce long-term dependencies, and yet admit parallelization, we introduce novel shallow RNNs. In this architecture, the first layer splits the input sequence and runs several independent RNNs.