Efficient Real-Time Inference in Temporal Convolution Networks

It has been recently demonstrated that Temporal Convolution Networks (TCNs) provide state-of-the-art results in many problem domains where the input data is a time-series. TCNs typically incorporate information from a long history of inputs (the receptive field) into a single output using many convolution layers. Real-time inference using a trained TCN can be challenging on devices with limited compute and memory, especially if the receptive field is large. This paper introduces the RT-TCN algorithm that reuses the output of prior convolution operations to minimize the computational requirements and persistent memory footprint of a TCN during real-time inference. We also show that when a TCN is trained using time slices of the input time-series, it can be executed in real-time continually using RT-TCN. In addition, we provide TCN architecture guidelines that ensure that real-time inference can be performed within memory and computational constraints.