Gated Recurrent Unit (GRU) is used as our recurrent unit which have fewer parameters than Long Short-Term Memory (LSTM).
Towards that goal, we take a system-theoretic perspective to design a new recurrent unit, which we call the prototypical recurrent unit (PRU).
Performances of vanilla LSTM are benchmarked with standard RNN and Gated Recurrent Unit (GRU) LSTM.
Our goal is to determine an adequate number of recurrent unit elements as well as to adjust their designable parameters so that the coupler footprint area is minimal.
This work aims at constructing an alternative recurrent unit that is as simple as possible and yet also captures the key components of LSTM/GRU recurrent units.
We employ convolutional neural network (CNN), recurrent structures such as recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU) and hybrid of CNN and recurrent structures to automatically detect the abnormality.
In this framework, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) are used for forecasting, based on the pollution and meteorological time series AirNet data.
Through a comparison of four different neural networks, namely multilayer perceptron (MLP), wavelet neural network (WNN), long short-term memory (LSTM) and gated recurrent unit (GRU), the LSTM and GRU present superior capabilities for multi-step-ahead time series prediction.
Five-layer RNNs with Gated Recurrent Unit (GRU) and sigmoid output layer are used as the second phase of our algorithm, which are extremely powerful machine learning tools capable of making full use of data fed to them.
The article develops upon the work of current end-to-end system by using gated recurrent unit in place of long short term memory which give similar accuracy but with lesser training time, further it also show the successfully use of a convolution based encoder for this task which gives results comparable to current state of the art system with much lesser training time.
Early benchmarking indicates that when using Intel Stratix 10 FPGAs, Brainwave can sustain 39.5 Teraflops on a large gated recurrent unit without any batching.
