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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 11·5 min read
Linearized 2-Simplicial Attention
TL;DR
A new linearized form of 2-simplicial attention was developed, achieving linear computational cost while maintaining global context. This model outperformed existing architectures, achieving the highest mean downstream accuracy and significantly reducing perplexity.
✦ Why It Matters
Consider adopting this linearized attention model to enhance your AI systems' performance and efficiency.
Key Takeaways
- Explore linearized 2-simplicial attention for efficient sequence processing.
- Implement custom Triton kernels for optimized performance.
- Combine with Kimi Delta Attention to eliminate softmax.
- Utilize the model to improve accuracy in downstream tasks.
How It Works
The linearized 2-simplicial attention reformulates the trilinear score as an inner product, allowing for efficient computation similar to softmax attention. By using positive random features, the model approximates the sum over one token axis, enabling a fixed-size state that retains historical context while keeping recent tokens explicit.
This design allows the model to scale linearly with sequence length while maintaining a global reach.