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The attention mechanisms of transformers effectively extract pertinentinformation from the input sequence. However, the quadratic complexity ofself-attention w.r.t the sequence length incurs heavy computational and memoryburdens, especially for tasks with long sequences. Existing accelerators faceperformance degradation in these tasks. To this end, we propose SALO to enablehybrid sparse attention mechanisms for long sequences. SALO contains a datascheduler to map hybrid sparse attention patterns onto hardware and a spatialaccelerator to perform the efficient attention computation. We show that SALOachieves 17.66x and 89.33x speedup on average compared to GPU and CPUimplementations, respectively, on typical workloads, i.e., Longformer and ViL.

SALO: An Efficient Spatial Accelerator Enabling Hybrid Sparse Attention Mechanisms for Long Sequences