PEOPLE

PRINT-SAFE: Printed Ultra-Low-Cost Electronic X-Design with Scalable Adaptive Fault Endurance
Pal, P.; Gheshlaghi, T.; Zhao, H.; Hefenbrock, M.; Beigl, M.; Tahoori, M. B.
2025. ACM Transactions on Embedded Computing Systems, 24 (5s), 1–22. doi:10.1145/3758096  

SpikeSynth: Energy-Efficient Adaptive Analog Printed Spiking Neural Networks
Pal, P.; Studt, A.; Gheshlaghi, T.; Hefenbrock, M.; Beigl, M.; Tahoori, M. B.
2025. 44th ACM/IEEE International Conference on Computer Aided Design (ICCAD 2025), Institute of Electrical and Electronics Engineers (IEEE)  

Power-Constrained Printed Neuromorphic Hardware Training
Gheshlaghi, T.; Zhao, H.; Pal, P.; Hefenbrock, M.; Beigl, M.; B. Tahoori, M.
2025. 62nd ACM/IEEE Design Automation Conference (DAC), San Francisco, CA, USA, 22-25 June 2025, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/DAC63849.2025.11132902  

Automatic Test Pattern Generation for Printed Neuromorphic Circuits
Gheshlaghi, T.; Pal, P.; Studt, A.; Hefenbrock, M.; Beigl, M.; B. Tahoori, M.
2025. 30th IEEE European Test Symposium (ETS ’25), 6 S., Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ETS63895.2025.11049604  

ADAPT-pNC: Mitigating Device Variability and Sensor Noise in Printed Neuromorphic Circuits with SO Adaptive Learnable Filters
Gheshlaghi, T.; Pal, P.; Zhao, H.; Hefenbrock, M.; Beigl, M.; Tahoori, M. B.
2025. 2025 Design, Automation & Test in Europe Conference (DATE), 1–7, Institute of Electrical and Electronics Engineers (IEEE). doi:10.23919/DATE64628.2025.10992786 

Neural Evolutionary Architecture Search for Compact Printed Analog Neuromorphic Circuits
Zhao, H.; Pal, P.; Hefenbrock, M.; Wang, Y.; Beigl, M.; Tahoori, M. B.
2025. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 1–14. doi:10.1109/TCAD.2024.3524357 

ADAPT-pNC: Mitigating Device Variability and Sensor Noise in Printed Neuromorphic Circuits with SO Adaptive Learnable Filters
Gheshlaghi, T.; Pal, P.; Zhao, H.; Hefenbrock, M.; Beigl, M.; Tahoori, M. B.
2025. Institute of Electrical and Electronics Engineers (IEEE). doi:10.5445/IR/1000178050  

Neural Architecture Search for Highly Bespoke Robust Printed Neuromorphic Circuits
Pal, P.; Zhao, H.; Gheshlaghi, T.; Hefenbrock, M.; Beigl, M.; Tahoori, M. B.
2024. ACM Digital Library. doi:10.5445/IR/1000172645  

Deep Neural Network Pruning with Progressive Regularizer
Zhou, Y.; Zhao, H.; Hefenbrock, M.; Li, S.; Beigl, M.
2024. 2024 IEEE International Joint Conference on Neural Networks (IJCNN), Yokohama, Japan, 30 June - 05 July 2024, 1–10, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/IJCNN60899.2024.10650997  

Fault Sensitivity Analysis of Printed Bespoke Multilayer Perceptron Classifiers
Pal, P.; Afentaki, F.; Zhao, H.; Saglam, G.; Hefenbrock, M.; Zervakis, G.; Beigl, M.; Tahoori, M. B.
2024. 29th IEEE European Test Symposium (ETS 2024), Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ETS61313.2024.10567964  

Analog Printed Spiking Neuromorphic Circuit
Pal, P.; Zhao, H.; Shatta, M.; Hefenbrock, M.; Mamaghani, S. B.; Nassif, S.; Beigl, M.; B. Tahoori, M.
2024. 2024 Design, Automation and Test in Europe Conference (DATE), 6 S., Institute of Electrical and Electronics Engineers (IEEE)  

Towards Temporal Information Processing – Printed Neuromorphic Circuits with Learnable Filters
Zhao, H.; Pal, P.; Hefenbrock, M.; Beigl, M.; Tahoori, M. B.
2023. 18th ACM International Symposium on Nanoscale Architectures (NANOARCH 2023), Dresden, 18.12 - 20.12.2023), Association for Computing Machinery (ACM). doi:10.5445/IR/1000164433  

Power-Aware Training for Energy-Efficient Printed Neuromorphic Circuits
Zhao, H.; Pal, P.; Hefenbrock, M.; Beigl, M.; Tahoori, M.
2023. 42nd IEEE/ACM International Conference on Computer-Aided Design, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICCAD57390.2023.10323917  

Highly-dependable printed neuromorphic circuits based on additive manufacturing
Zhao, H.; Hefenbrock, M.; Beigl, M.; Tahoori, M. B.
2023. Flexible and Printed Electronics, 8 (8), Art.-Nr.: 025018. doi:10.1088/2058-8585/acd8cd  

Highly Bespoke Robust Printed Neuromorphic Circuits
Zhao, H.; Sapui, B.; Hefenbrock, M.; Yang, Z.; Beigl, M.; Tahoori, M. B.
2023. 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE), Antwerp, Belgium,17-19 April 2023, Institute of Electrical and Electronics Engineers (IEEE). doi:10.23919/DATE56975.2023.10137298  

Split Additive Manufacturing for Printed Neuromorphic Circuits
Zhao, H.; Hefenbrock, M.; Beigl, M.; Tahoori, M. B.
2023. 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE), Antwerp, Belgium, 17-19 April 2023, Institute of Electrical and Electronics Engineers (IEEE). doi:10.23919/DATE56975.2023.10136891  

McXai: Local Model-Agnostic Explanation As Two Games
Huang, Y.; Schaal, N.; Hefenbrock, M.; Zhou, Y.; Riedel, T.; Beigl, M.
2023. 2023 International Joint Conference on Neural Networks (IJCNN), 01–08, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/IJCNN54540.2023.10191756 

Universal Distributional Decision-Based Black-Box Adversarial Attack with Reinforcement Learning
Huang, Y.; Zhou, Y.; Hefenbrock, M.; Riedel, T.; Fang, L.; Beigl, M.
2023. Neural Information Processing – 29th International Conference, ICONIP 2022, Virtual Event, November 22–26, 2022, Proceedings. Part III. Ed.: M. Tanveer, 206 – 215, Springer International Publishing. doi:10.1007/978-3-031-30111-7_18 

Automatic Feature Engineering Through Monte Carlo Tree Search
Huang, Y.; Zhou, Y.; Hefenbrock, M.; Riedel, T.; Fang, L.; Beigl, M.
2023. Machine Learning and Knowledge Discovery in Databases – European Conference, ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings, Part III. Ed.: M.-R. Amini, 581–598, Springer Nature Switzerland. doi:10.1007/978-3-031-26409-2_35 

Aging-Aware Training for Printed Neuromorphic Circuits
Zhao, H.; Hefenbrock, M.; Beigl, M.; Tahoori, M. B.
2022. International Conference on Computer Aided Design (ICCAD ’22), October 30-November 3, 2022, San Diego, CA, USA, Art.-No.: 38, Association for Computing Machinery (ACM). doi:10.1145/3508352.3549411  

TinyHAR: A Lightweight Deep Learning Model Designed for Human Activity Recognition
Zhou, Y.; Zhao, H.; Huang, Y.; Hefenbrock, M.; Riedel, T.; Beigl, M.
2022. International Symposium on Wearable Computers (ISWC’22) , Atlanta, GA and Cambridge, UK, September 11-15, 2022, 89–93, Association for Computing Machinery (ACM). doi:10.1145/3544794.3558467  

In-situ Tuning of Printed Neural Networks for Variation Tolerance
Hefenbrock, M.; Weller, D. D.; Aghassi-Hagmann, J.; Beigl, M.; Tahoori, M. B.
2022. Proceedings of the 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE 2022). Ed.: C. Bolchini, 72–75, Institute of Electrical and Electronics Engineers (IEEE). doi:10.23919/DATE54114.2022.9774591 

Fast and Efficient High-Sigma Yield Analysis and Optimization using Kernel Density Estimation on a Bayesian Optimized Failure Rate Model
Weller, D. D.; Hefenbrock, M.; Beigl, M.; Tahoori, M. B.
2022. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 41 (3), 695–708. doi:10.1109/TCAD.2021.3064440 

Counterfeit Detection and Prevention in Additive Manufacturing Based on Unique Identification of Optical Fingerprints of Printed Structures
Erozan, A. T.; Hefenbrock, M.; Gnad, D. R. E.; Beigl, M.; Aghassi-Hagmann, J.; Tahoori, M. B.
2022. IEEE Access, 10, 105910–105919. doi:10.1109/ACCESS.2022.3209241  

Improving Human Activity Recognition Models by Learnable Sparse Wavelet Layer
Zhao, H.; Zhou, Y.; Riedel, T.; Hefenbrock, M.; Beigl, M.
2022. International Symposium on Wearable Computers (ISWC’22) , Atlanta, GA and Cambridge, UK, September 11-15, 2022, 84–88, Association for Computing Machinery (ACM). doi:10.1145/3544794.3558461  

McXai: Local model-agnostic explanation as two games
Huang, Y.; Schaal, N.; Hefenbrock, M.; Zhou, Y.; Riedel, T.; Fang, L.; Beigl, M.
2022 

Realization and training of an inverter-based printed neuromorphic computing system
Weller, D. D.; Hefenbrock, M.; Beigl, M.; Aghassi-Hagmann, J.; Tahoori, M. B.
2021. Scientific Reports, 11 (1), 9554. doi:10.1038/s41598-021-88396-0  

Automatic Remaining Useful Life Estimation Framework with Embedded Convolutional LSTM as the Backbone
Zhou, Y.; Hefenbrock, M.; Huang, Y.; Riedel, T.; Beigl, M.
2021. Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track : European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part IV. Ed.: Y. Dong, 461–477, Springer International Publishing. doi:10.1007/978-3-030-67667-4_28 

Bayesian Optimized Mixture Importance Sampling for High-Sigma Failure Rate Estimation
Weller, D. D.; Hefenbrock, M.; Golanbari, M. S.; Beigl, M.; Aghassi-Hagmann, J.; Tahoori, M. B.
2020. IEEE transactions on computer-aided design of integrated circuits and systems, 39 (10), 2772–2783. doi:10.1109/tcad.2019.2961321 

Scalable selection of EEG features for compression
Tsurugasaki, Y.; Shimoda, K.; Hefenbrock, M.; Taya, A.; Song, S.; Tobe, Y.
2020. 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2020 ACM International Symposium on Wearable Computers, UbiComp/ISWC 2020, Virtual, Online, Mexico, 12 - 17 September 2020, 712–715, Association for Computing Machinery (ACM). doi:10.1145/3410530.3414438 

Fast and Accurate High-Sigma Failure Rate Estimation through Extended Bayesian Optimized Importance Sampling
Hefenbrock, M.; Weller, D. D.; Beigl, M.; Tahoori, M. B.
2020. 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE). Ed.: G. Di Natale, 103–108, Institute of Electrical and Electronics Engineers (IEEE). doi:10.23919/DATE48585.2020.9116242 

Automatic Remaining Useful Life Estimation Framework with Embedded Convolutional LSTM as the Backbone
Zhou, Y.; Gao, Y.; Huang, Y.; Hefenbrock, M.; Riedel, T.; Beigl, M.
2020 

Programmable Neuromorphic Circuit based on Printed Electrolyte-Gated Transistors
Weller, D. D.; Hefenbrock, M.; Tahoori, M. B.; Aghassi-Hagmann, J.; Beigl, M.
2020. 2020 25th Asia and South Pacific Design Automation Conference (ASP-DAC), 446–451, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ASP-DAC47756.2020.9045211 

Reverse Engineering of Printed Electronics Circuits: From Imaging to Netlist Extraction
Erozan, A. T.; Hefenbrock, M.; Beigl, M.; Aghassi-Hagmann, J.; Tahoori, M. B.
2020. IEEE transactions on information forensics and security / Institute of Electrical and Electronics Engineers, 15, 475–486. doi:10.1109/TIFS.2019.2922237 

Crossover-aware placement and routing for inkjet printed circuits
Rasheed, F.; Hefenbrock, M.; Bishnoi, R.; Beigl, M.; Aghassi-Hagmann, J.; Tahoori, M. B.
2020. ACM journal on emerging technologies in computing systems, 16 (2), Article No.19. doi:10.1145/3375461 

Impact of Intrinsic Capacitances on the Dynamic Performance of Printed Electrolyte-Gated Inorganic Field Effect Transistors
Feng, X.; Punckt, C.; Marques, G. C.; Hefenbrock, M.; Tahoori, M. B.; Aghassi-Hagmann, J.
2019. IEEE transactions on electron devices, 66 (8), 3365–3370. doi:10.1109/TED.2019.2919933 

Predictive Modeling and Design Automation of Inorganic Printed Electronics
Rasheed, F.; Hefenbrock, M.; Bishnoi, R.; Beigl, M.; Aghassi-Hagmann, J.; Tahoori, M. B.
2019. Design, Automation and Test in Europe Conference and Exhibition (DATE 2019), Florenz, Italien, 25.–29. März 2019 

Predictive Modeling and Design Automation of Inorganic Printed Electronics
Rasheed, F.; Hefenbrock, M.; Bishnoi, R.; Beigl, M.; Aghassi-Hagmann, J.; Tahoori, M. B.
2019. Proceedings of the 2019 Design, Automation & Test in Europe (DATE), 25-29 March 2019, Florence, Italy, 30–35, Institute of Electrical and Electronics Engineers (IEEE). doi:10.23919/DATE.2019.8715159 

Bayesian Optimized Importance Sampling for High Sigma Failure Rate Estimation
Weller, D. D.; Hefenbrock, M.; Golanbari, M. S.; Beigl, M.; Tahoori, M. B.
2019. Proceedings of the 2019 Design, Automation & Test in Europe (DATE), 25-29 March 2019, Florence, Italy, 1667–1672, Institute of Electrical and Electronics Engineers (IEEE). doi:10.23919/DATE.2019.8714879 

Variability Modeling for Printed Inorganic Electrolyte-Gated Transistors and Circuits
Rasheed, F.; Hefenbrock, M.; Beigl, M.; Tahoori, M. B.; Aghassi-Hagmann, J.
2019. IEEE transactions on electron devices, 66 (1), 146–152. doi:10.1109/TED.2018.2867461 

Experimental characterization of charge storage behavior of electrolyte gated field effect transistors based on oxide semiconductor
Feng, X.; Punckt, C.; Hefenbrock, M.; Tahoori, M.; Aghassi-Hagmann, J.
2018. Internationale Fachmesse und Kongress für gedruckte Elektronik (LOPEC 2018), München, Deutschland, 14.–15. März 2018 

Vibration Personalization with Evolutionary Algorithms
Pescara, E.; Hefenbrock, M.; Rzepka, T.; Beigl, M.
2018. 2018 Joint ACM International Conference on Pervasive and Ubiquitous Computing, UbiComp 2018 and 2018 ACM International Symposium on Wearable Computers, ISWC 2018; Singapore; Singapore; 8 October 2018 through 12 October 2018, 436–439, Association for Computing Machinery (ACM). doi:10.1145/3267305.3267619