ML For Hardware Design Interpretability: Challenges and Opportunities

ML For Hardware Design Interpretability: Challenges and Opportunities

11 April 2025

Raymond Baartmans

Andrew Ensinger

Victor Agostinelli

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Papers citing "ML For Hardware Design Interpretability: Challenges and Opportunities"

16 / 16 papers shown

Title
DeepRTL: Bridging Verilog Understanding and Generation with a Unified Representation Model Yi Liu Changran Xu Yunhao Zhou Zhiyu Li Qiang Xu VLM 122 7 0 20 Feb 2025
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning DeepSeek-AI Daya Guo Dejian Yang Haowei Zhang Junxiao Song ... Shiyu Wang S. Yu Shunfeng Zhou Shuting Pan S.S. Li ReLM VLM OffRL AI4TS LRM 380 2,000 0 22 Jan 2025
Natural language is not enough: Benchmarking multi-modal generative AI for Verilog generation Kaiyan Chang Zhirong Chen Yunhao Zhou Wenlong Zhu Kun Wang ... Mengdi Wang Shengwen Liang Huawei Li Yinhe Han Ying Wang 80 6 0 11 Jul 2024
New Solutions on LLM Acceleration, Optimization, and Application Yingbing Huang Lily Jiaxin Wan Hanchen Ye Manvi Jha Jinghua Wang Yuhong Li Xiaofan Zhang Deming Chen 75 12 0 16 Jun 2024
RepoAgent: An LLM-Powered Open-Source Framework for Repository-level Code Documentation Generation Qinyu Luo Yining Ye Shihao Liang Zhong Zhang Yujia Qin ... Yankai Lin Yingli Zhang Xiaoyin Che Zhiyuan Liu Maosong Sun LLMAG 121 46 0 26 Feb 2024
Gated Linear Attention Transformers with Hardware-Efficient Training Aaron Courville Bailin Wang Songlin Yang Yikang Shen Yoon Kim 124 180 0 11 Dec 2023
Natural Language based Context Modeling and Reasoning for Ubiquitous Computing with Large Language Models: A Tutorial Haoyi Xiong Jiang Bian Sijia Yang Xiaofei Zhang Linghe Kong Daqing Zhang LRM LLMAG 75 5 0 24 Sep 2023
Two-stage LLM Fine-tuning with Less Specialization and More Generalization Yihan Wang Si Si Daliang Li Michal Lukasik Felix X. Yu Cho-Jui Hsieh Inderjit S Dhillon Sanjiv Kumar 137 30 0 01 Nov 2022
CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation Yue Wang Weishi Wang Shafiq Joty Guosheng Lin 299 1,592 0 02 Sep 2021
Optimising Design Verification Using Machine Learning: An Open Source Solution B. Varambally N. Sehgal 27 8 0 04 Dec 2020
GraphCodeBERT: Pre-training Code Representations with Data Flow Daya Guo Shuo Ren Shuai Lu Zhangyin Feng Duyu Tang ... Dawn Drain Neel Sundaresan Jian Yin Daxin Jiang M. Zhou 167 1,152 0 17 Sep 2020
Survey of Machine Learning Accelerators Albert Reuther Peter Michaleas Michael Jones V. Gadepally S. Samsi J. Kepner 56 136 0 01 Sep 2020
Language Models are Few-Shot Learners Tom B. Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared Kaplan ... Christopher Berner Sam McCandlish Alec Radford Ilya Sutskever Dario Amodei BDL 904 42,463 0 28 May 2020
Training with Quantization Noise for Extreme Model Compression Angela Fan Pierre Stock Benjamin Graham Edouard Grave Remi Gribonval Hervé Jégou Armand Joulin MQ 106 246 0 15 Apr 2020
Calibration of Pre-trained Transformers Shrey Desai Greg Durrett UQLM 319 301 0 17 Mar 2020
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 321 1,875 0 03 Feb 2017