  The attention mechanism requires huge computational efforts to process
unnecessary calculations, significantly limiting the system's performance.
Researchers propose sparse attention to convert some DDMM operations to SDDMM
and SpMM operations. However, current sparse attention solutions introduce
massive off-chip random memory access. We propose CPSAA, a novel crossbar-based
PIM-featured sparse attention accelerator. First, we present a novel attention
calculation mode. Second, we design a novel PIM-based sparsity pruning
architecture. Finally, we present novel crossbar-based methods. Experimental
results show that CPSAA has an average of 89.6X, 32.2X, 17.8X, 3.39X, and 3.84X
performance improvement and 755.6X, 55.3X, 21.3X, 5.7X, and 4.9X energy-saving
when compare with GPU, FPGA, SANGER, ReBERT, and ReTransformer.
