arXiv:2603.03612v3 Announce Type: replace-cross Abstract: The community is increasingly exploring linear RNNs (LRNNs) as language models, motivated by their expressive power and parallelizability. While prior work establishes the expressivity benefits of LRNNs over transformers, it is unclear what makes LRNNs -- but not traditional, nonlinear RNNs -- as easy to parallelize in practice as transformers. We answer this question by providing a tight connection between types of RNNs and standard complexity classes. We show that LRNNs can be viewed as log-depth (bounded fan-in) arithmetic circuits,
The AI community is actively seeking more efficient and scalable architectures for large language models to overcome the limitations of current transformer-based systems.
Improved parallelizability and efficiency in RNN architectures could significantly impact the development and deployment costs of future AI models, potentially democratizing access to powerful AI.
The understanding of why certain neural network architectures are more amenable to parallel processing is refined, pointing to potential new directions for AI hardware and software co-design.
- · AI hardware manufacturers
- · Cloud providers
- · AI research labs
- · Developers of large language models
- · Inefficient AI architectures
- · AI companies reliant on older RNN paradigms
Research and development accelerate into linear RNNs and similar parallelizable architectures.
New AI accelerators designed specifically for these efficient architectures emerge, potentially altering the competitive landscape of compute.
The reduced computational overhead allows for the training of even larger, more complex AI models, leading to unexpected capabilities and applications.
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