Fetching the paper…
Reading the bibliography…
As large language models (LLMs) like ChatGPT exhibited unprecedented machine intelligence, it also shows great performance in assisting hardware engineers to realize higher-efficiency logic design via natural language interaction.
Z. Manna and R. Waldinger, “Synthesis: Dreams → programs,” IEEE Transactions on Software Engineering
1979
Earlier work this paper cites.
G. Martin and G. Smith, “High-level synthesis: Past, present, and future,” IEEE Design & Test of Computers
2009
Earlier work this paper cites.
J. Bachrach, H. Vo, B. Richards, Y. Lee, A. Waterman, R. Avižienis, J. Wawrzynek, and K. Asanović, “Chisel: constructing hardware in a scala embedded language,” Design Automation Conference
2012
Earlier work this paper cites.
R. Alur, R. Bodik, G. Juniwal, M. M. K. Martin, M. Raghothaman, S. A. Seshia, R. Singh, A. Solar-Lezama, E. Torlak, and A. Udupa, “Syntax-guided synthesis,” Formal Methods in Computer-Aided Design
2013
Earlier work this paper cites.
B. Finkbeiner and S. Schewe, “Bounded synthesis,” International Journal on Software Tools for Technology Transfer
2013
Earlier work this paper cites.
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?,” Advances in neural information processing systems
2014
Earlier work this paper cites.
P.-M. Osera and S. Zdancewic, “Type-and-example-directed program synthesis,” SIGPLAN Not
2015
Earlier work this paper cites.
J. K. Feser, S. Chaudhuri, and I. Dillig, “Synthesizing data structure transformations from input-output examples,” ACM SIGPLAN Conference on Programming Language Design and Implementation
2015
Earlier work this paper cites.
P. Faymonville, B. Finkbeiner, and L. Tentrup, “Bosy: An experimentation framework for bounded synthesis,” Computer Aided Verification: 29th International Conference
2017
Earlier work this paper cites.
D. Koeplinger, M. Feldman, R. Prabhakar, Y. Zhang, S. Hadjis, R. Fiszel, T. Zhao, L. Nardi, A. Pedram, C. Kozyrakis, and K. Olukotun, “Spatial: A language and compiler for application accelerators,” SIGPLAN Not
2018
Cited alongside, same era.
T. Chen, T. Moreau, Z. Jiang, L. Zheng, E. Yan, H. Shen, M. Cowan, L. Wang, Y. Hu, L. Ceze, C. Guestrin, and A. Krishnamurthy, “TVM: An automated End-to-End optimizing compiler for deep learning,” USENIX Symposium on Operating Systems Design and Implementation
2018
Cited alongside, same era.
N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. De Laroussilhe, A. Gesmundo, M. Attariyan, and S. Gelly, “Parameter-efficient transfer learning for nlp,” International Conference on Machine Learning
2019
Cited alongside, same era.
Y.-H. Lai, H. Rong, S. Zheng, W. Zhang, X. Cui, Y. Jia, J. Wang, B. Sullivan, Z. Zhang, Y. Liang, et al
2020
Cited alongside, same era.
E. J. Hu, yelong shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen, “LoRA: Low-rank adaptation of large language models,” International Conference on Learning Representations
2022
Later among the works it cites.
D. Guo, S. Lu, N. Duan, Y. Wang, M. Zhou, and J. Yin, “Unixcoder: Unified cross-modal pre-training for code representation,” Annual Meeting of the Association for Computational Linguistics
2022
Later among the works it cites.
J. Wei, X. Wang, D. Schuurmans, M. Bosma, brian ichter, F. Xia, E. H. Chi, Q. V. Le, and D. Zhou, “Chain of thought prompting elicits reasoning in large language models,” Advances in Neural Information Processing Systems
2022
Later among the works it cites.
L. community, “Circuit ir compilers and tools.” https://github.com/llvm/circt , 2023
2023
Closest in time.
A. Magyar, A. Kalantar, B. Christian, Blaok, and B. J. et al., “Xls: Accelerated hw synthesis.” https://github.com/google/xls/ , 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Z. Feng, D. Guo, D. Tang, N. Duan, X. Feng, M. Gong, L. Shou, B. Qin, T. Liu, D. Jiang, et al
2020
Cited alongside, same era.
Y. Wang, W. Wang, S. Joty, and S. C. Hoi, “Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation,” Conference on Empirical Methods in Natural Language Processing
2021
Cited alongside, same era.
D. Guo, S. Ren, S. Lu, Z. Feng, D. Tang, S. LIU, L. Zhou, N. Duan, A. Svyatkovskiy, S. Fu, M. Tufano, S. K. Deng, C. Clement, D. Drain, N. Sundaresan, J. Yin, D. Jiang, and M. Zhou, “Graphcodebert: Pre-training code representations with data flow,” International Conference on Learning Representations
2021
Cited alongside, same era.
H. Ye, C. Hao, J. Cheng, H. Jeong, J. Huang, S. Neuendorffer, and D. Chen, “Scalehls: A new scalable high-level synthesis framework on multi-level intermediate representation,” IEEE International Symposium on High-Performance Computer Architecture
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2023
Closest in time.
Y. Zhou, A. I. Muresanu, Z. Han, K. Paster, S. Pitis, H. Chan, and J. Ba, “Large language models are human-level prompt engineers,” International Conference on Learning Representations
2023
Closest in time.
D. Zhou, N. Schärli, L. Hou, J. Wei, N. Scales, X. Wang, D. Schuurmans, C. Cui, O. Bousquet, Q. V. Le, and E. H. Chi, “Least-to-most prompting enables complex reasoning in large language models,” International Conference on Learning Representations
2023
Closest in time.
2023
Closest in time.
Anthropic, “Claude in slack.” https://www.anthropic.com/claude-in-slack , 2023
2023
Closest in time.