  This article does not propose any novel algorithm or new hardware for
sparsity. Instead, it aims to serve the "common good" for the increasingly
prosperous Sparse Neural Network (SNN) research community. We attempt to
summarize some most common confusions in SNNs, that one may come across in
various scenarios such as paper review/rebuttal and talks - many drawn from the
authors' own bittersweet experiences! We feel that doing so is meaningful and
timely, since the focus of SNN research is notably shifting from traditional
pruning to more diverse and profound forms of sparsity before, during, and
after training. The intricate relationships between their scopes, assumptions,
and approaches lead to misunderstandings, for non-experts or even experts in
SNNs. In response, we summarize ten Q\&As of SNNs from many key aspects,
including dense vs. sparse, unstructured sparse vs. structured sparse, pruning
vs. sparse training, dense-to-sparse training vs. sparse-to-sparse training,
static sparsity vs. dynamic sparsity, before-training/during-training vs.
post-training sparsity, and many more. We strive to provide proper and
generically applicable answers to clarify those confusions to the best extent
possible. We hope our summary provides useful general knowledge for people who
want to enter and engage with this exciting community; and also provides some
"mind of ease" convenience for SNN researchers to explain their work in the
right contexts. At the very least (and perhaps as this article's most
insignificant target functionality), if you are writing/planning to write a
paper or rebuttal in the field of SNNs, we hope some of our answers could help
you!
