ESACT: An End-to-End Sparse Accelerator for Compute-Intensive Transformers via Local Similarity

Hongxiang Liu, Zhifang Deng, Tong Pu +1

Transformers, composed of QKV generation, attention computation, and FFNs, have become the dominant model across various domains due to their outstanding performance. However, thei…