Hyft: A Reconfigurable Softmax Accelerator with Hybrid Numeric Format for both Training and Inference

Author:

Xia Tianhua1ORCID,Zhang Sai Qian1ORCID

Affiliation:

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

Publisher

ACM

Reference18 articles.

1. Tri Dao et al. 2022. FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness. arXiv:2205.14135 [cs.LG]

2. Jacob Devlin et al. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv:1810.04805 [cs.CL]

3. Alexey Dosovitskiy et al. 2021. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv:2010.11929 [cs.CV]

4. Yue Gao et al. 2020. Design and Implementation of an Approximate Softmax Layer for Deep Neural Networks. In 2020 ISCAS. 1--5.

5. Edward J Hu et al. 2021. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021).

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