  Transformer model architectures have garnered immense interest lately due to
their effectiveness across a range of domains like language, vision and
reinforcement learning. In the field of natural language processing for
example, Transformers have become an indispensable staple in the modern deep
learning stack. Recently, a dizzying number of "X-former" models have been
proposed - Reformer, Linformer, Performer, Longformer, to name a few - which
improve upon the original Transformer architecture, many of which make
improvements around computational and memory efficiency. With the aim of
helping the avid researcher navigate this flurry, this paper characterizes a
large and thoughtful selection of recent efficiency-flavored "X-former" models,
providing an organized and comprehensive overview of existing work and models
across multiple domains.
