  Deep learning time-series processing often relies on convolutional neural
networks with overlapping windows. This overlap allows the network to produce
an output faster than the window length. However, it introduces additional
computations. This work explores the potential to optimize computational
efficiency during inference by exploiting convolution's shift-invariance
properties to skip the calculation of layer activations between successive
overlapping windows. Although convolutions are shift-invariant, zero-padding
and pooling operations, widely used in such networks, are not efficient and
complicate efficient streaming inference. We introduce StreamiNNC, a strategy
to deploy Convolutional Neural Networks for online streaming inference. We
explore the adverse effects of zero padding and pooling on the accuracy of
streaming inference, deriving theoretical error upper bounds for pooling during
streaming. We address these limitations by proposing signal padding and pooling
alignment and provide guidelines for designing and deploying models for
StreamiNNC. We validate our method in simulated data and on three real-world
biomedical signal processing applications. StreamiNNC achieves a low deviation
between streaming output and normal inference for all three networks (2.03 -
3.55% NRMSE). This work demonstrates that it is possible to linearly speed up
the inference of streaming CNNs processing overlapping windows, negating the
additional computation typically incurred by overlapping windows.
