# What is the big fuzz about SHA-RNN versus Transformers?

In his paper introducing SHA-RNN (https://arxiv.org/pdf/1911.11423.pdf) Stephen Merity states that neglecting one direction of research (in this case LSTMs) over another (transformers) merily because the SOTA in transformers are due to using more computing power is not the way to go.

I agree that finding neat tricks in AI/ML is equally (if not more) important than just throwing more computing power at the problem. However I am a little bit confused.

The main difference (since they both use attention units) between his SHA-RNN and transformers seem to be the fact that SHA-RNN uses LSTMs to "encode the position of words", where transformers do position encoding by using cosine and sine functions.

My confusion comes from the fact that LSTMs need to be handled sequentially and thus they cannot use this large advantage of GPUs, being able to compute things in parallel, whilst transformers can. Wouldn't this mean that (assuming LSTMs and positional encoding are able to acquire the same results), training using LSTMs would take longer than transformers and thus need more computing power, thus defeating the initial puporse of this paper? Or am I misinterpreting this?

Basically my question comes down to "Why would an SHA-RNN be less computationally expensive than a transformer?"