SRU: A Simple Recurrent Unit for Efficient Deep Learning

Introduction:

SRU, or Simple Recurrent Unit, is a type of recurrent neural network that simplifies the computations involved to enable faster and more efficient deep learning. Unlike traditional recurrent neural networks like LSTM and GRU, which are based on complex computations and often require significant computational resources, SRU presents a simpler model that provides high parallelism and independent dimensions to improve the model's efficiency. In this article, we will explore the architecture of SRU, how it differs from other recurrent neural networks, and its advantages in deep learning.

SRU Architecture:

SRU involves a simple computation model that involves the following:

  • A sequence of input vectors,
  • A forget gate that controls the flow of information,
  • A state vector that captures sequential information,
  • A current observation that is adaptively averaged using the previous state and the current input vector,
  • A high speed highway network.

These components work together to enable a simple and efficient deep learning algorithm.

The Computation Model:

A single layer of SRU consists of the following computation:

Source: Simple Recurrent Unit (SRU).

Here, $\mathbf{W}, \mathbf{W}_{f}$ and $\mathbf{W}_{r}$ are parameter matrices, while $\mathbf{v}_{f}, \mathbf{v}_{r}, \mathbf{b}_{f}$ and $\mathbf{b}_{v}$ are parameter vectors that are learned during training.

The computation model takes a sequence of input vectors, $\mathbf{x}_{t}$, which are used to compute the sequence of states, $\mathbf{c}_{t}$, capturing sequential information. The computation model involves a forget gate, $\mathbf{f}_{t}$, which controls the information flow. The state vector, $\mathbf{c}_{t}$, is calculated adaptively by averaging the previous state, $\mathbf{c}_{t-1}$, and the current observation, $\mathbf{W}\mathbf{x}_{t}$, based on $\mathbf{f}_{t}$.

The highway network is a high-speed component that enables deep recurrent models training by employing highway connections and a parameter initialization scheme tailored for gradient propagation in deep architectures. The light recurrence and highway network components work together to simplify the computations involved and enable efficient deep learning.

The Advantages of SRU:

SRU offers several advantages over traditional recurrent neural networks:

  • Higher parallelism and efficient computations
  • Simplification of traditional computations
  • Improved training of deep recurrent models
  • Efficient use of modern GPUs
  • Improvement of computational speed and reduction of computational resources
  • Equivalent capacity to other recurrent neural networks, while using fewer computations and hyper-parameters.

Conclusion:

SRU is a simple and efficient algorithm for deep learning that improves on traditional recurrent neural networks by presenting a simpler computation model. The parallelism and independent dimensions offered by SRU significantly reduce computational resources while providing similar modeling capacity as other recurrent neural networks. SRU presents an excellent option for those looking to deploy deep learning models in real-time, particularly for applications with resource constraints.

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