First figure from our paper: how the LSTM with a twist allows for the equivalent speed of a plain convnet by running efficiently in parallel on GPUs, like image processing convents.[1]
Best of all, as it's only an "LSTM with (these) twists", it's drop-in compatible with existing LSTMs but can get you a 2-17 times speed-up over NVIDIA's cuDNN LSTM - essentially speed equivalent to the TCN or WaveNet speed-up.
That's why Baidu implemented QRNN in their production Deep Voice 2 neural text-to-speech (TTS) system[3].
This isn't to say TCN or QRNN is better, simply that it's dangerous to flat out say _no_ if you're not actually certain or don't correctly recall the underlying information.
Disclaimer: I'm the co-author of the QRNN.
Double disclaimer: The TCN paper cites the QRNN but decides not to test against it. They also show results over one of my datasets.
[1]: https://www.semanticscholar.org/paper/Quasi-Recurrent-Neural...
[2]: https://github.com/salesforce/pytorch-qrnn
[3]: https://www.semanticscholar.org/paper/Deep-Voice-2%3A-Multi-...
One suggestion to the authors: the benchmark figures are interesting, but I wish you had shown CPU only results also. At work, I have all the GPU resources I need but for my home projects, which are all NLP deep learning experiments, I usually rent a many core large memory server with no GPUs (GPUs seem to speed up RNNs less than other model types).