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SSD algorithm

The structured state space duality algorithm, commonly known as the SSD algorithm, is a sequence-processing method in deep learning that unifies state space models and attention mechanisms through structured matrix operations. By representing sequence transformations as multiplications with semiseparable matrices, the algorithm divides an input sequence into uniform chunks or blocks. It computes local interactions within each block using dense matrix multiplications tailored for specialized accelerator hardware, such as tensor cores, while passing contextual information across blocks via efficient linear state transitions. This design combines the linear computational complexity of recurrent state space models with the high parallel throughput of attention-style matrix multiplication, allowing neural networks to process long sequences efficiently with expanded hidden-state capacities.

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