  Recurrent Neural Networks (RNNs) are a class of machine learning algorithms
used for applications with time-series and sequential data. Recently, there has
been a strong interest in executing RNNs on embedded devices. However,
difficulties have arisen because RNN requires high computational capability and
a large memory space. In this paper, we review existing implementations of RNN
models on embedded platforms and discuss the methods adopted to overcome the
limitations of embedded systems.
  We will define the objectives of mapping RNN algorithms on embedded platforms
and the challenges facing their realization. Then, we explain the components of
RNN models from an implementation perspective. We also discuss the
optimizations applied to RNNs to run efficiently on embedded platforms.
Finally, we compare the defined objectives with the implementations and
highlight some open research questions and aspects currently not addressed for
embedded RNNs.
  Overall, applying algorithmic optimizations to RNN models and decreasing the
memory access overhead is vital to obtain high efficiency. To further increase
the implementation efficiency, we point up the more promising optimizations
that could be applied in future research. Additionally, this article observes
that high performance has been targeted by many implementations, while
flexibility has, as yet, been attempted less often. Thus, the article provides
some guidelines for RNN hardware designers to support flexibility in a better
manner.
