Deep learning based on neural networks has been widely used in image recognition,
speech recognition, natural language processing, automatic driving, and other fields and …

Abstract “Recurrent neural networks”(RNNs) are powerful artificial intelligence models that
have shown remarkable effectiveness in several tasks such as music generation, speech …

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 …

Attention-based neural networks have become pervasive in many AI tasks. Despite their
excellent algorithmic performance, the use of the attention mechanism and feedforward …

Long short-term memory (LSTM) recurrent networks are frequently used for tasks involving
time-sequential data, such as speech recognition. Unlike previous LSTM accelerators that …

Deep neural network (DNN) accelerators as an example of domain-specific architecture
have demonstrated great success in DNN inference. However, the architecture acceleration …

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

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

We empirically evaluate an undervolting technique, ie, underscaling the circuit supply
voltage below the nominal level, to improve the power-efficiency of Convolutional Neural …

Series arc fault is a common phenomenon in the power system, it will directly affect the
working reliability, but there is no mature method to detect it due to its concealment and …