  Recurrent Neural Networks (RNNs) are an important class of neural networks
designed to retain and incorporate context into current decisions. RNNs are
particularly well suited for machine learning problems in which context is
important, such as speech recognition and language translation. This work
presents RNNFast, a hardware accelerator for RNNs that leverages an emerging
class of non-volatile memory called domain-wall memory (DWM). We show that DWM
is very well suited for RNN acceleration due to its very high density and low
read/write energy. At the same time, the sequential nature of input/weight
processing of RNNs mitigates one of the downsides of DWM, which is the linear
(rather than constant) data access time.RNNFast is very efficient and highly
scalable, with flexible mapping of logical neurons to RNN hardware blocks. The
basic hardware primitive, the RNN processing element (PE) includes custom
DWM-based multiplication, sigmoid and tanh units for high density and
low-energy. The accelerator is designed to minimize data movement by closely
interleaving DWM storage and computation. We compare our design with a
state-of-the-art GPGPU and find21.8x higher performance with70x lower energy
