  Convolutional Neural Networks are extensively used in a wide range of
applications, commonly including computer vision tasks like image and video
classification, recognition, and segmentation. Recent research results
demonstrate that multilayer(deep) networks involving mono-dimensional
convolutions and dilation can be effectively used in time series and sequences
classification and segmentation, as well as in tasks involving sequence
modelling. These structures, commonly referred to as Temporal Convolutional
Networks (TCNs), have been demonstrated to consistently outperform Recurrent
Neural Networks in terms of accuracy and training time [1]. While FPGA-based
inference accelerators for classic CNNs are widespread, literature is lacking
in a quantitative evaluation of their usability on inference for TCN models. In
this paper we present such an evaluation, considering a CNN accelerator with
specific features supporting TCN kernels as a reference and a set of
state-of-the-art TCNs as a benchmark. Experimental results show that, during
TCN execution, operational intensity can be critical for the overall
performance. We propose a convolution scheduling based on batch processing that
can boost efficiency up to 96% of theoretical peak performance. Overall we can
achieve up to 111,8 GOPS/s and power efficiency of 33,9 GOPS/s/W on an
Ultrascale+ ZU3EG (up to 10x speedup and 3x power efficiency improvement with
respect to pure software implementation).
