The effectiveness of Recurrent Neural Networks (RNNs) for tasks such as Automatic Speech
Recognition has fostered interest in RNN inference acceleration. Due to the recurrent nature …

This work introduces Remarn, a reconfigurable multi-threaded multi-core accelerator
supporting both spatial and temporal co-execution of Recurrent Neural Network (RNN) …

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 …

Recurrent neural networks (RNNs) are becoming the de-facto solution for speech
recognition. RNNs exploit long-term temporal relationships in data by applying repeated …

Over the past decade, Deep Learning (DL) and Deep Neural Network (DNN) have gone
through a rapid development. They are now vastly applied to various applications and have …

Recurrent neural networks (RNNs) have been proven effective for sequence-based tasks
thanks to their capability to process temporal information. In real-world systems, deep RNNs …

Recurrent neural networks (RNNs) based automatic speech recognition has nowadays
become promising and important on mobile devices such as smart phones. However …

Abstract Recurrent Neural Networks (RNNs) have been successful in a wide range of
applications involving temporal sequences such as natural language processing, speech …

Recurrent neural networks (RNNs) are extensively employed to perform inference based on
the temporal features of the input data. However, their computational workload and power …

Recurrent Neural Networks (RNNs) have continued to facilitate rapid progress in a variety of
academic and industrial fields, though their complexity continues to make efficient …