  The attention mechanisms of transformers effectively extract pertinent
information from the input sequence. However, the quadratic complexity of
self-attention w.r.t the sequence length incurs heavy computational and memory
burdens, especially for tasks with long sequences. Existing accelerators face
performance degradation in these tasks. To this end, we propose SALO to enable
hybrid sparse attention mechanisms for long sequences. SALO contains a data
scheduler to map hybrid sparse attention patterns onto hardware and a spatial
accelerator to perform the efficient attention computation. We show that SALO
achieves 17.66x and 89.33x speedup on average compared to GPU and CPU
implementations, respectively, on typical workloads, i.e., Longformer and ViL.
