1. Brown, T.B., et al.: Language Models are Few-Shot Learners (2020).arxiv.org/abs/2005.14165

2. Chitty-Venkata, K.T., et al.: A survey of techniques for optimizing transformer inference. J. Syst. Archit.144, 102990 (2023)

   Article  Google Scholar 

3. Devlin, J., et al.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings 2019 Conference of the North American Chapter of the ACL: Human Language Technologies, vol. 1, pp. 4171–4186 (2019)

   Google Scholar 

4. Dongarra, J.J., Du Croz, J., Hammarling, S., Hanson, R.J.: An extended set of FORTRAN basic linear algebra subprograms. ACM Trans. Math. Softw.14(1), 1–17 (1988)

   Article  Google Scholar 

5. Dosovitskiy, A., et al.: An Image is Worth\(16\times 16\) Words: Transformers for Image Recognition at Scale (2021).arxiv.org/abs/2010.11929

6. Goto, K., van de Geijn, R.A.: Anatomy of high-performance matrix multiplication. ACM Trans. Math. Softw.34(3), 12:1-12:25 (2008)

   Article MathSciNet  Google Scholar 

7. Hennessy, J.L., Patterson, D.A.: A new golden age for computer architecture. Comm. ACM62(2), 48–60 (2019)

   Article  Google Scholar 

8. Igual, F., Piñuel, L., Catalán, S., Martínez, H., Castelló, A., Quintana-Ortí, E.: Automatic generation of micro-kernels for performance portability of matrix multiplication on RISC-V vector processors. In: Proceedings of the SC ’23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis, pp. 1523–1532. SC-W ’23, ACM, New York, NY, USA (2023)

   Google Scholar 

9. Pati, S., et al.: Demystifying BERT: system design implications. In: 2022 IEEE International Symposium Workload Characterization (IISWC), pp. 296–309 (2022)

   Google Scholar 

10. Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res.21(1), (2020)

    Google Scholar 

11. Touvron, H., Cord, M., Jégou, H.: DeiT III: Revenge of the ViT. In: Computer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXIV, pp. 516–533. Springer-Verlag, Berlin, Heidelberg (2022)

    Google Scholar 

12. Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst.30, 5998–6008 (2017)

    Google Scholar 

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