A Scalable BFloat16 Dot-Product Architecture for Deep Learning

Author:

Zhang Jing1,Huang Libo1,Tan Hongbing1,Zheng Zhong1,Guo Hui1

Affiliation:

1. National University of Defense Technology, China

Funder

NSFC

NSFHN

Publisher

ACM

Reference8 articles.

1. A BF16 FMA is All You Need for DNN Training

2. Dhiraj D. Kalamkar and et al. A study of bfloat16 for deep learning training. ArXiv, abs/1905.12322 , 2019 . Dhiraj D. Kalamkar and et al. A study of bfloat16 for deep learning training. ArXiv, abs/1905.12322, 2019.

3. Neil Burgess and et al. Bfloat16 processing for neural networks . 2019 IEEE 26th Symposium on Computer Arithmetic (ARITH) , pages 88 -- 91 , 2019 . Neil Burgess and et al. Bfloat16 processing for neural networks. 2019 IEEE 26th Symposium on Computer Arithmetic (ARITH), pages 88--91, 2019.

4. Mach and et al. Fpnew: An open-source multiformat floating-point unit architecture for energy-proportional transprecision computing;Stefan;IEEE Transactions on Very Large Scale Integration (VLSI) Systems,2020

5. Luca Bertaccini and et al. MiniFloat-NN and ExSdotp: An ISA extension and a modular open hardware unit for low-precision training on RISC-V cores . 2022 IEEE 29th Symposium on Computer Arithmetic (ARITH) , pages 1 -- 8 , 2022 . Luca Bertaccini and et al. MiniFloat-NN and ExSdotp: An ISA extension and a modular open hardware unit for low-precision training on RISC-V cores. 2022 IEEE 29th Symposium on Computer Arithmetic (ARITH), pages 1--8, 2022.

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. SFDoP: A Scalable Fused BFloat16 Dot-Product Architecture for DNN;2023 IEEE 41st International Conference on Computer Design (ICCD);2023-11-06

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