Aaren: Rethinking Attention As Recurrent Neural Network Rnn For Efficient Sequence Modeling On Low-resource Devices

This article discusses the limitations of traditional recurrent neural networks (RNNs) in handling sequential data and the challenges of deploying resource-intensive models like Transformers in low-resource environments. It explores various attention-based models and methods, such as RWKV, RetNet, and Linear Transformer, and proposes efficient algorithms for enhancing attention mechanisms in sequence modeling.