A 28nm Scalable and Flexible Accelerator for Sparse Transformer Models

Author:

Liao Yuan1ORCID,Meng Jian2ORCID,Seo Jae-sun1ORCID

Affiliation:

1. Cornell Tech, Cornell University, New York, NY, United States

2. Cornell Tech, Cornell University, New York City, NY, United States

Funder

SRC/DARPA, Samsung Electronics

Publisher

ACM

Reference14 articles.

1. Ashish Vaswani et al. 2017. Attention is All you Need. In NeurIPS.

2. Eunji Yoo et al. 2023. TF-MVP: Novel Sparsity-Aware Transformer Accelerator with Mixed-Length Vector Pruning. In DAC. 1--6.

3. Guan Shen et al. 2022. SALO: An Efficient Spatial Accelerator Enabling Hybrid Sparse Attention Mechanisms for Long Sequences. In DAC. 571--576.

4. Hanrui Wang et al. 2021. SpAtten: Efficient Sparse Attention Architecture with Cascade Token and Head Pruning. In HPCA. 97--110.

5. Kyungmin Kim et al. 2021. Rethinking the Self-Attention in Vision Transformers. In CVPR Workshops. 3065--3069.

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