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In High-Level Synthesis (HLS), converting a regular C/C++ program into its HLS-compatible counterpart (HLS-C) still requires tremendous manual effort.
David B. Thomas, “Synthesisable recursion for C++ HLS tools,” IEEE International Conference on Application-specific Systems, Architectures and Processors (ASAP), 2016
2016
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Zeping Xue, David B. Thomas, “SynADT: Dynamic data structures in high level synthesis,” IEEE Symposium on Field Programmable Custom Computing Machines (FCCM), 2016
2016
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Hsuan Hsiao, Jason H. Anderson, “Sensei: An area-reduction advisor for FPGA high-level synthesis,” IEEE/ACM Design, Automation & Test in Europe Conference & Exhibition (DATE), 2018
2018
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David B. Thomas, “Templatised Soft Floating-Point for High-Level Synthesis,” IEEE Symposium on Field Programmable Custom Computing Machines (FCCM), 2019
2019
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Nicholas V. Giamblanco, Jason H. Anderson, “A Dynamic Memory Allocation Library for High-Level Synthesis,” IEEE/ACM International Conference on Field-Programmable Logic and Applications (FPL), 2019
2019
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Nils Reimers, Iryna Gurevych, “Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks,” ACL Empirical Methods in Natural Language Processing (EMNLP), 2019
2019
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Nadesh Ramanathan, George A. Constantinides and John Wickerson, “Precise pointer analysis in high-level synthesis,” IEEE/ACM International Conference on Field-Programmable Logic and Applications (FPL), 2020
2020
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Jason Lau, Aishwarya Sivaraman, Qian Zhang, Muhammad Ali Gulzar, Jason Cong, Miryung Kim, “Refactoring for Heterogeneous Computing with FPGA,” IEEE/ACM International Conference on Software Engineering (ICSE), 2020
2020
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Qi Sun, Tinghuan Chen, Siting Liu, Jianli Chen, Hao Yu, and Bei Yu, “Correlated Multi-objective Multi-fidelity Optimization for HLS Directives Design,” ACM Transactions on Design Automation of Electronic Systems (TODAES), 2022
2022
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Atefeh Sohrabizadeh, Cody Hao Yu, Min Gao, and Jason Cong, “AutoDSE: Enabling Software Programmers to Design Efficient FPGA Accelerators,” ACM Transactions on Design Automation of Electronic Systems (TODAES), 2022
2022
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Long Ouyang, Jeff Wu, Xu Jiang, Ryan Lowe et al., “Training language models to follow instructions with human feedback,” Advances in Neural Information Processing Systems (NeurIPS), 2022
2022
Cited alongside, same era.
Yunsheng Bai, Atefeh Sohrabizadeh, Zongyue Qin, Ziniu Hu, Yizhou Sun, Jason Cong, “Towards a Comprehensive Benchmark for High-Level Synthesis Targeted to FPGAs,” Advances in Neural Information Processing Systems (NeurIPS), 2023
2023
Cited alongside, same era.
“Leetcode Problem Set,” Accessed: 2023. [Online]. Available: https://leetcode.com/problemset/
2023
Cited alongside, same era.
“Siemens EDA Catapult Synthesis User and Reference Manual,” Accessed: 2023. [Online]. Available: https://support.sw.siemens.com/
2023
Cited alongside, same era.
Chunqiu Steven Xia, Yuxiang Wei, Lingming Zhang, “Automated program repair in the era of large pre-trained language models,” IEEE/ACM International Conference on Software Engineering (ICSE), 2023
2023
Later among the works it cites.
Tinghuan Chen, Grace Li Zhang, Bei Yu, Bing Li, Ulf Schlichtmann, “Machine Learning in Advanced IC Design: A Methodological Survey,” IEEE Design & Test, 2023
2023
Later among the works it cites.
“Siemens EDA Catapult High-Level Synthesis Tools,” Accessed: 2023. [Online]. Available: https://eda.sw.siemens.com/en-US/ic/catapult-high-level-synthesis/hls/
2023
Later among the works it cites.
Kangwei Xu, Grace Li Zhang, Ulf Schlichtmann, Bing Li, “Logic Design of Neural Networks for High-Throughput and Low-Power Applications,” IEEE/ACM Asia and South Pacific Design Automation Conference (ASP-DAC), 2024
2024
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2023
Cited alongside, same era.
Jason Blocklove, Siddharth Garg, Ramesh Karri, Hammond Pearce, “Chip-Chat: Challenges and Opportunities in Conversational Hardware Design,” IEEE Workshop on Machine Learning for CAD (MLCAD), 2023
2023
Cited alongside, same era.
Shailja Thakur, Jason Blocklove, Hammond Pearce, Benjamin Tan, Siddharth Garg, Ramesh Karri, “AutoChip: Automating HDL Generation Using LLM Feedback,” arXiv preprint: 2311.04887, 2023
2023
Cited alongside, same era.
Matthew Jin, Syed Shahriar, Michele Tufano, Xin Shi, Shuai Lu, Neel Sundaresan, and Alexey Svyatkovskiy, “InferFix: End-to-End Program Repair with LLMs,” ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2023
2023
Cited alongside, same era.
Hammond Pearce, Benjamin Tan, Baleegh Ahmad, Ramesh Karri, Brendan Dolan-Gavitt, “Examining Zero-Shot Vulnerability Repair with Large Language Models,” IEEE Symposium on Security and Privacy, 2023
2023
Cited alongside, same era.
Harshit Joshi, Jose Cambronero, Sumit Gulwani, Vu Le, Ivan Radicek, Gust Verbruggen, “Repair is nearly generation: multilingual program repair with LLMs,” Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), 2023
2023
Cited alongside, same era.
2024
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Yao Lu, Shang Liu, Qijun Zhang, Zhiyao Xie, ”RTLLM: An Open-Source Benchmark for Design RTL Generation with Large Language Model,” IEEE/ACM Asia and South Pacific Design Automation Conference, 2024
2024
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Yun-Da Tsai, Mingjie Liu, Haoxing Ren, “Automatically Fixing RTL Syntax Errors with Large Language Model,” IEEE/ACM Design Automation Conference (DAC), 2024
2024
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He Ye, Martin Monperrus, “ITER: Iterative Neural Repair for Multi-Location Patches,” IEEE/ACM International Conference on Software Engineering (ICSE), 2024
2024
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“GPT-4 Turbo through OpenAI API,” Accessed: 2024. [Online]. Available: https://platform.openai.com/
2024
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