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

RNNFast: An Accelerator for Recurrent Neural Networks Using Domain Wall Memory