I will take two examples to illustrate the discussion.

So from the hardware accelerator design perspective
If you take a look at the BitFusion Paper : Github –> https://github.com/hsharma35/bitfusion
: ArXiv –> https://arxiv.org/pdf/1712.01507

If you look at the Quantized Neural networks example
Github — > https://github.com/MatthieuCourbariaux/BinaryNet
https://github.com/itayhubara/BinaryNet
ArXiv — > https://arxiv.org/pdf/1609.07061

So one can see from reading the papers and the code what tools were used for the experimentation. Here the first one uses some hardware modelling tools (like CACTI) to get preliminary results. In the case of the latter they use torch and theano and modify it to get the results (as far as I understand).

So are there any other suggestions that the Machine learning community would like to mention?