Recurrent neural network from adder's perspective: Carry-lookahead RNN

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Recurrent Neural Networks (RNNs) are essential for sequence modeling but suffer from inherent serial dependencies, limiting computational parallelization and efficiency.
• This limitation mirrors challenges faced by early serial adders in digital electronics.

Purpose of the Study:

• To address the inefficiency of traditional RNNs by introducing parallel computation capabilities.
• To explore the architectural similarities between RNNs and serial adders to inspire a new model.

Main Methods:

• Drawing inspiration from carry-lookahead adders, a carry-lookahead module was integrated into the RNN architecture.
• A novel parallel computation method for RNNs was designed and implemented, leading to the proposed Carry-lookahead RNN (CL-RNN).

Main Results:

• The proposed CL-RNN demonstrates significant advantages in parallelism and offers a flexible receptive field.
• Empirical evaluations confirm that CL-RNN outperforms existing typical RNNs on sequence modeling tasks specifically designed for RNNs.

Conclusions:

• CL-RNN offers a viable solution to enhance the parallelizability and efficiency of recurrent neural networks.
• The findings suggest CL-RNN is a promising advancement for various sequence modeling applications.