# CPSAA: Accelerating Sparse Attention using Crossbar-based Processing-In-Memory Architecture

arXiv: https://arxiv.org/abs/2210.06696 · HTML: https://mlsanity.com/paper/2210.06696/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2022.

## Built on by (most cited first, 1 of 1)

- [LLM Inference on IMC-NoC Architecture with Balanced Dataflow and Fine-Grained Parallelism](https://mlsanity.com/paper/2609.00857/) (2026)

## Abstract (arXiv)

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.