Simple Recurrent Units for Highly Parallelizable Recurrence (1709.02755v5)

Abstract: Common recurrent neural architectures scale poorly due to the intrinsic difficulty in parallelizing their state computations. In this work, we propose the Simple Recurrent Unit (SRU), a light recurrent unit that balances model capacity and scalability. SRU is designed to provide expressive recurrence, enable highly parallelized implementation, and comes with careful initialization to facilitate training of deep models. We demonstrate the effectiveness of SRU on multiple NLP tasks. SRU achieves 5--9x speed-up over cuDNN-optimized LSTM on classification and question answering datasets, and delivers stronger results than LSTM and convolutional models. We also obtain an average of 0.7 BLEU improvement over the Transformer model on translation by incorporating SRU into the architecture.

• The paper presents the SRU architecture, decoupling state dimensions to enable high parallelism and achieve a 5-9x speed-up on NLP tasks.
• It simplifies recurrent computations using point-wise multiplications while preserving the ability to capture sequential dependencies.
• Empirical evaluations show SRU matching or outperforming LSTM/GRU models and even improving BLEU scores in machine translation.

Simple Recurrent Units for Highly Parallelizable Recurrence

This paper introduces the Simple Recurrent Unit (SRU), a novel recurrent architecture designed to address the limitations of existing recurrent neural networks (RNNs) in terms of scalability and parallelization. Traditional recurrent architectures like Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs) suffer from inherent sequential dependencies that hinder parallel execution, significantly impacting their computational efficiency. SRU aims to strike a balance between model expressiveness and the ability to leverage modern hardware acceleration, such as GPUs.

The SRU architecture exhibits several key characteristics:

1. Parallelization: Unlike LSTMs and GRUs, which have inherent sequential dependencies in their state computations, SRU allows for high levels of parallelism. Each dimension of the state vector is independent in SRU, facilitating CUDA-level optimizations that parallelize computations across hidden dimensions and time steps.
2. Light Recurrence: SRU simplifies the traditional recurrent computations by using a point-wise multiplication for connections between hidden states. This simplification does not compromise its ability to capture sequential information, as demonstrated through an empirical evaluation on natural language processing tasks.
3. Effectiveness on Multiple NLP Tasks: The paper reports results on several benchmarks, including text classification, question answering, machine translation, and character-level LLMing. SRU consistently outperforms or matches more complex models while being computationally efficient. Notably, it achieves a 5-9x speed-up over cuDNN-optimized LSTM on classification and question answering datasets and provides a 0.7 BLEU improvement over the Transformer in translation tasks.
4. Model Design and Initialization: The SRU incorporates features like highway networks and a tailored initialization scheme, enhancing gradient flow and stabilizing training. The initialization strategy ensures consistent variance of hidden activations as the model depth increases, addressing challenges commonly faced in training deep networks.
5. Empirical Evaluation and Analysis: The paper provides a thorough empirical evaluation of SRU against other RNN architectures, such as LSTMs, GRUs, and convolutional networks. It also details ablation studies demonstrating the contribution of individual components, such as skip connections and scaling corrections, to the overall performance of the architecture.

Implications and Future Directions

The SRU architecture presents practical implications for both the research and application of RNNs in NLP and beyond. By offering a scalable and parallelizable solution, SRU empowers the development of deeper and more computationally feasible models. This advancement is particularly relevant in applications requiring real-time processing or those limited by hardware constraints.

Theoretically, SRU challenges the prevailing notion that the complex recurrence of traditional RNNs is necessary for capturing temporal dependencies. By achieving comparable or superior performance with simplified recurrence, SRU fosters a re-evaluation of architectural complexity against computational efficiency in recurrent models.

Looking ahead, the development and refinement of SRU-like architectures could lead to broader adoption in AI systems that integrate sequential components. Future research may explore integration with other neural architectures, such as attention mechanisms, or adaptations tailored to specific tasks and domain challenges. As AI applications grow increasingly diverse, solutions like SRU that optimize both computational and representational efficiency will be crucial in advancing the deployment and capabilities of machine learning models.