Aaren: A new approach to attention as a recurrent neural network RNN ​​for efficient sequence modeling on low-resource devices

Sequence modeling is a key field in machine learning, with applications such as reinforcement learning, time series forecasting, and event prediction. These models are designed to handle data where the order of the input data is significant, making them essential for tasks such as robotics, financial forecasting, and medical diagnoses. Traditionally, recurrent neural networks (RNNs) have been used for their ability to efficiently process sequential data despite limitations in parallel processing.

The rapid development of machine learning has highlighted the limitations of existing models, especially in resource-constrained environments. Known for their exceptional performance and ability to exploit GPU parallelism, transformers are resource-intensive, making them unsuitable for low-resource applications such as mobile and embedded devices. The main challenge is the square-memory and computational requirements that make them difficult to implement in scenarios with limited computational resources.

Existing work includes several attention-based models and methods. Transformers, despite their high efficiency, require large resources. Approximations such as RWKV, RetNet, and linear transformer offer attention linearization to improve performance, but have limitations in terms of token error. Attention can be computed cyclically, as shown by Rabe and Staats, and softmax-based attention can be reformulated as an RNN. Efficient algorithms for computing prefix scans, such as those developed by Hillis and Steele, provide fundamental techniques for enhancing attention mechanisms in sequence modeling. However, these techniques must fully take into account the inherent resource intensity, especially in applications involving long sequences such as climate data analysis and economic forecasting. This has led to the search for alternative methods of maintaining efficiency while being more resource efficient.

Researchers from Mila and Borealis AI introduced attention as a recurrent neural network (Aaren), a novel method that reinterprets the attention mechanism as a form of RNN. This innovative approach retains the benefits of parallel Transformers training while allowing for efficient updates with new tokens. Unlike traditional RNNs that process data sequentially and have scalability issues, Aaren uses a parallel prefix scanning algorithm to compute attention scores more efficiently, handling sequential data with constant memory requirements. This makes Aaren particularly suitable for low-resource environments where computational performance is paramount.

In detail, Aaren views the attention mechanism as a many-to-one RNN. Conventional attention methods compute results in parallel, requiring linear memory for the number of tokens. However, Aaren introduces a new method for computing attention in the form of many-to-many RNN, significantly reducing memory consumption. This is achieved through a parallel prefix scanning algorithm that allows Aaren to process multiple context tokens simultaneously, effectively updating its state. Attention output is computed using a series of associative operations, which ensures that the memory and computation load remains constant regardless of the sequence length.

The performance of Aaren has been empirically tested on various tasks, demonstrating its effectiveness and robustness. For reinforcement learning tasks, Aaren has been tested on 12 datasets in the D4RL benchmark, including environments such as HalfCheetah, Ant, Hopper, and Walker. The results showed that Aaren achieved competitive performance with Transformers, achieving scores such as 42.16 ± 1.89 for medium datasets in the HalfCheetah environment. This performance extends to event forecasting, where Aaren was evaluated on eight popular datasets. For example, on the Reddit dataset, Aaren achieved a negative log likelihood (NLL) of 0.31 ± 0.30, demonstrating performance comparable to Transformers but with reduced computational overhead.

Aaren has been tested on eight real-world datasets for time series forecasting, including weather, exchange, traffic, and ECL. For the weather dataset, Aaren achieved a mean squared error (MSE) of 0.24 ± 0.01 and a mean absolute error (MAE) of 0.25 ± 0.01 for a prediction length of 192, demonstrating its ability to process data efficiently time series. Similarly, Aaren outperformed Transformers on ten datasets from the UEA Time Series Classification Archive for time series classification, demonstrating its versatility and effectiveness.

In summary, Aaren is significantly advancing sequence modeling in resource-constrained environments. By combining the parallel training capabilities of Transformers with an efficient RNN update mechanism, Aaren provides a sustainable solution that maintains high performance while being computationally efficient. This makes it an ideal choice for applications in low-resource environments where traditional models are not sufficient.

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