| 2026 | DATE | GCPT: Gradient-aware Clustering Method for Efficient Post-Training Quantization in Large Neural Networks. | Chuyi Dai, Chen Ye, Zeyu Li, Jun Tao, Wei Zhang, Grace Li Zhang, Xin Li |
| 2026 | DATE | Late Breaking Results: Conversion of Neural Networks into Logic Flows for Edge Computing. | Daniel Stein, Shaoyi Huang, Rolf Drechsler, Bing Li, Grace Li Zhang |
| 2026 | GECCO | LLM-NAS: LLM-driven Hardware-Aware Neural Architecture Search. | Hengyi Zhu, Grace Li Zhang, Shaoyi Huang |
| 2025 | ASPDAC | An Efficient General-Purpose Optical Accelerator for Neural Networks. | Sijie Fei, Amro Eldebiky, Grace Li Zhang, Bing Li, Ulf Schlichtmann |
| 2025 | DATE | CorrectBench: Automatic Testbench Generation with Functional Self-Correction using LLMs for HDL Design. | Ruidi Qiu, Grace Li Zhang, Rolf Drechsler, Ulf Schlichtmann, Bing Li |
| 2025 | ETS | Large Language Models (LLMs) for Verification, Testing, and Design. | Chandan Kumar Jha, Muhammad Hassan, Khushboo Qayyum, Sallar Ahmadi-Pour, Kangwei Xu, Ruidi Qiu, Jason Blocklove, Luca Collini, Andre Nakkab, Ulf Schlichtmann, Grace Li Zhang, Ramesh Karri, Bing Li, Siddharth Garg, Rolf Drechsler |
| 2025 | ICCAD | HLSTester: Efficient Testing of Behavioral Discrepancies with LLMs for High-Level Synthesis. | Kangwei Xu, Bing Li, Grace Li Zhang, Ulf Schlichtmann |
| 2025 | ICCAD | Revolution or Hype? Seeking the Limits of Large Models in Hardware Design. | Qiang Xu, Leon Stok, Rolf Drechsler, Xi Wang, Grace Li Zhang, Igor L. Markov |
| 2025 | ICLR | Basis Sharing: Cross-Layer Parameter Sharing for Large Language Model Compression. | Jingcun Wang, Yu-Guang Chen, Ing-Chao Lin, Bing Li, Grace Li Zhang |
| 2024 | ASPDAC | Logic Design of Neural Networks for High-Throughput and Low-Power Applications. | Kangwei Xu, Grace Li Zhang, Ulf Schlichtmann, Bing Li |
| 2024 | DATE | Computational and Storage Efficient Quadratic Neurons for Deep Neural Networks. | Chuangtao Chen, Grace Li Zhang, Xunzhao Yin, Cheng Zhuo, Ulf Schlichtmann, Bing Li |
| 2024 | DATE | A FeFET-based Time-Domain Associative Memory for Multi-bit Similarity Computation. | Qingrong Huang, Hamza Errahmouni Barkam, Zeyu Yang, Jianyi Yang, Thomas Kmpfe, Kai Ni, Grace Li Zhang, Bing Li, Ulf Schlichtmann, Mohsen Imani, Cheng Zhuo, Xunzhao Yin |
| 2024 | DATE | ScanCamouflage: Obfuscating Scan Chains with Camouflaged Sequential and Logic Gates. | Tarik Ibrahimpasic, Grace Li Zhang, Michaela Brunner, Georg Sigl, Bing Li, Ulf Schlichtmann |
| 2024 | DATE | Class-Aware Pruning for Efficient Neural Networks. | Mengnan Jiang, Jingcun Wang, Amro Eldebiky, Xunzhao Yin, Cheng Zhuo, Ing-Chao Lin, Grace Li Zhang |
| 2024 | DATE | OplixNet: Towards Area-Efficient Optical Split-Complex Networks with Real-to-Complex Data Assignment and Knowledge Distillation. | Ruidi Qiu, Amro Eldebiky, Grace Li Zhang, Xunzhao Yin, Cheng Zhuo, Ulf Schlichtmann, Bing Li |
| 2024 | ICCAD | BasisN: Reprogramming-Free RRAM-Based In-Memory-Computing by Basis Combination for Deep Neural Networks. | Amro Eldebiky, Grace Li Zhang, Xunzhao Yin, Cheng Zhuo, Ing-Chao Lin, Ulf Schlichtmann, Bing Li |
| 2023 | DAC | PowerPruning: Selecting Weights and Activations for Power-Efficient Neural Network Acceleration. | Richard Petri, Grace Li Zhang, Yiran Chen, Ulf Schlichtmann, Bing Li |
| 2023 | DATE | CorrectNet: Robustness Enhancement of Analog In-Memory Computing for Neural Networks by Error Suppression and Compensation. | Amro Eldebiky, Grace Li Zhang, Georg Bcherer, Bing Li, Ulf Schlichtmann |
| 2023 | DATE | Countering Uncertainties in In-Memory-Computing Platforms with Statistical Training, Accuracy Compensation and Recursive Test. | Amro Eldebiky, Grace Li Zhang, Bing Li |
| 2023 | DATE | Class-based Quantization for Neural Networks. | Wenhao Sun, Grace Li Zhang, Huaxi Gu, Bing Li, Ulf Schlichtmann |
| 2023 | DATE | SteppingNet: A Stepping Neural Network with Incremental Accuracy Enhancement. | Wenhao Sun, Grace Li Zhang, Xunzhao Yin, Cheng Zhuo, Huaxi Gu, Bing Li, Ulf Schlichtmann |
| 2023 | ICCAD | A Novel and Efficient Block-Based Programming for ReRAM-Based Neuromorphic Computing. | Wei-Lun Chen, Fang-Yi Gu, Ing-Chao Lin, Grace Li Zhang, Bing Li, Ulf Schlichtmann |
| 2023 | ICCAD | NearUni: Near-Unitary Training for Efficient Optical Neural Networks. | Amro Eldebiky, Bing Li, Grace Li Zhang |
| 2022 | DAC | Energy efficient data search design and optimization based on a compact ferroelectric FET content addressable memory. | Jiahao Cai, Mohsen Imani, Kai Ni, Grace Li Zhang, Bing Li, Ulf Schlichtmann, Cheng Zhuo, Xunzhao Yin |
| 2022 | DSD | RRAM-based Neuromorphic Computing: Data Representation, Architecture, Logic, and Programming. | Grace Li Zhang, Shuhang Zhang, Hai Helen Li, Ulf Schlichtmann |
| 2022 | ISCAS | Aging Aware Retraining for Memristor-based Neuromorphic Computing. | Wenwen Ye, Grace Li Zhang, Bing Li, Ulf Schlichtmann, Cheng Zhuo, Xunzhao Yin |
| 2021 | ASPDAC | Robustness of Neuromorphic Computing with RRAM-based Crossbars and Optical Neural Networks. | Grace Li Zhang, Bing Li, Ying Zhu, Tianchen Wang, Yiyu Shi, Xunzhao Yin, Cheng Zhuo, Huaxi Gu, Tsung-Yi Ho, Ulf Schlichtmann |
| 2021 | DAC | Bayesian Inference Based Robust Computing on Memristor Crossbar. | Di Gao, Qingrong Huang, Grace Li Zhang, Xunzhao Yin, Bing Li, Ulf Schlichtmann, Cheng Zhuo |
| 2021 | DATE | Energy-Aware Designs of Ferroelectric Ternary Content Addressable Memory. | Yu Qian, Zhenhao Fan, Haoran Wang, Chao Li, Mohsen Imani, Kai Ni, Grace Li Zhang, Bing Li, Ulf Schlichtmann, Cheng Zhuo, Xunzhao Yin |
| 2021 | DATE | Hardware-Software Codesign of Weight Reshaping and Systolic Array Multiplexing for Efficient CNNs. | Jingyao Zhang, Huaxi Gu, Grace Li Zhang, Bing Li, Ulf Schlichtmann |
| 2021 | DATE | An Efficient Programming Framework for Memristor-based Neuromorphic Computing. | Grace Li Zhang, Bing Li, Xing Huang, Chen Shen, Shuhang Zhang, Florin Burcea, Helmut Graeb, Tsung-Yi Ho, Hai Li, Ulf Schlichtmann |
| 2021 | ICCAD | Reliable Memristor-based Neuromorphic Design Using Variation- and Defect-Aware Training. | Di Gao, Grace Li Zhang, Xunzhao Yin, Bing Li, Ulf Schlichtmann, Cheng Zhuo |
| 2020 | ASPDAC | Timing Resilience for Efficient and Secure Circuits. | Grace Li Zhang, Michaela Brunner, Bing Li, Georg Sigl, Ulf Schlichtmann |
| 2020 | DATE | Statistical Training for Neuromorphic Computing using Memristor-based Crossbars Considering Process Variations and Noise. | Ying Zhu, Grace Li Zhang, Tianchen Wang, Bing Li, Yiyu Shi, Tsung-Yi Ho, Ulf Schlichtmann |
| 2020 | ICCAD | PathDriver: A Path-Driven Architectural Synthesis Flow for Continuous-Flow Microfluidic Biochips. | Xing Huang, Youlin Pan, Grace Li Zhang, Bing Li, Wenzhong Guo, Tsung-Yi Ho, Ulf Schlichtmann |
| 2020 | ICCAD | Countering Variations and Thermal Effects for Accurate Optical Neural Networks. | Ying Zhu, Grace Li Zhang, Bing Li, Xunzhao Yin, Cheng Zhuo, Huaxi Gu, Tsung-Yi Ho, Ulf Schlichtmann |
| 2019 | DATE | Aging-aware Lifetime Enhancement for Memristor-based Neuromorphic Computing. | Shuhang Zhang, Grace Li Zhang, Bing Li, Hai Helen Li, Ulf Schlichtmann |
| 2018 | DAC | Virtualsync: timing optimization by synchronizing logic waves with sequential and combinational components as delay units. | Grace Li Zhang, Bing Li, Masanori Hashimoto, Ulf Schlichtmann |
| 2018 | DATE | TimingCamouflage: Improving circuit security against counterfeiting by unconventional timing. | Grace Li Zhang, Bing Li, Bei Yu, David Z. Pan, Ulf Schlichtmann |
| 2016 | DAC | EffiTest: efficient delay test and statistical prediction for configuring post-silicon tunable buffers. | Grace Li Zhang, Bing Li, Ulf Schlichtmann |
| 2016 | DATE | Sampling-based buffer insertion for post-silicon yield improvement under process variability. | Grace Li Zhang, Bing Li, Ulf Schlichtmann |
| 2016 | ICCAD | PieceTimer: a holistic timing analysis framework considering setup/hold time interdependency using a piecewise model. | Grace Li Zhang, Bing Li, Ulf Schlichtmann |