Wiersema, T. (2021). Guaranteeing Properties of Reconfigurable Hardware Circuits with Proof-Carrying Hardware. Paderborn University.

Lienen, C., &; Platzner, M. (2021). Design of Distributed Reconfigurable Robotics Systems with ReconROS. ACM Transactions on Reconfigurable Technology and Systems, 1–20.https://doi.org/10.1145/3494571 

Kashikar, C. (2021). A Comparison of Machine Learning Techniques for the On-line Characterization of Tasks Executed on Heterogeneous Compute Nodes. Paderborn University.

Awais, M., Ghasemzadeh Mohammadi, H., &; Platzner, M. (2021). LDAX: A Learning-based Fast Design Space Exploration Framework for Approximate Circuit Synthesis. In Proceedings of the ACM Great Lakes Symposium on VLSI (GLSVLSI) 2021 (pp. 27–32). Virtual: ACM.https://doi.org/10.1145/3453688.3461506 

Rehnen, J. W. (2021). Decomposition of Arithmetic Components for the Approximate Circuit Synthesis with EvoApproxLib.

Awais, M., &; Platzner, M. (2021). MCTS-Based Synthesis Towards Efficient Approximate Accelerators. Proceedings of IEEE Computer Society Annual Symposium on VLSI, 384–389.

Brede, M. (2021). Implementation and Profiling of XCS in the Context of Embedded Systems. Paderborn: Paderborn University.

Witschen, L. M., Wiersema, T., Raeisi Nafchi, M., Bockhorn, A., &; Platzner, M. (n.d.). Timing Optimization for Virtual FPGA Configurations. In F. Hannig, S. Derrien, P. Diniz, &; D. Chillet (Eds.), Proceedings of International Symposium on Applied Reconfigurable Computing (ARC’21). Springer Lecture Notes in Computer Science.https://doi.org/10.1007/978-3-030-79025-7_4 

Boschmann, A., Neuhaus, D., Vogt, S., Kaltschmidt, C., Platzner, M., &; Dosen, S. (2021). Immersive augmented reality system for the training of pattern classification control with a myoelectric prosthesis. Journal of NeuroEngineering and Rehabilitation, 18(1), Article 25.https://doi.org/10.1186/s12984-021-00822-6 

Rodriguez, A., Otero, A., Platzner, M., &; De la Torre, E. (2021). Exploiting Hardware-Based Data-Parallel and Multithreading Models for Smart Edge Computing in Reconfigurable FPGAs. IEEE Transactions on Computers, 1–1.https://doi.org/10.1109/tc.2021.3107196 

Hansmeier, T. (2021). Self-aware Operation of Heterogeneous Compute Nodes using the Learning Classifier System XCS. HEART ’21: Proceedings of the 11th International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies. International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies (HEART ’21), Online.https://doi.org/10.1145/3468044.3468055 

Sheikh, M. A. (2021). Design and Implementation of a ReconROS-based Obstacle Avoidance System. Paderborn University.

Lienen, C., &; Platzner, M. (2021). Design of Distributed Reconfigurable Robotics Systems with ReconROS. In arXiv:2107.07208.

Hansmeier, T., &; Platzner, M. (2021). An Experimental Comparison of Explore/Exploit Strategies for the Learning Classifier System XCS. GECCO ’21: Proceedings of the Genetic and Evolutionary Computation Conference Companion, 1639–1647.https://doi.org/10.1145/3449726.3463159 

Jakobs, M.-C., Pauck, F., Platzner, M., Wehrheim, H., &; Wiersema, T. (2021). Software/Hardware Co-Verification for Custom Instruction Set Processors. IEEE Access.https://doi.org/10.1109/ACCESS.2021.3131213 

Ahmed, Q. A. (2021). Hardware Trojans in Reconfigurable Computing. 2021 IFIP/IEEE 29th International Conference on Very Large Scale Integration (VLSI-SoC).https://doi.org/10.1109/vlsi-soc53125.2021.9606974 

Ahmed, Q. A., Wiersema, T., &; Platzner, M. (2021). Malicious Routing: Circumventing Bitstream-level Verification for FPGAs. 2021 Design, Automation &; Test in Europe Conference &; Exhibition (DATE). Design, Automation and Test in Europe Conference (DATE’21), Alpexpo | Grenoble, France.https://doi.org/10.23919/DATE51398.2021.9474026 

Clausing, L. (2021). ReconO: High-Performance Embedded Computing for Industrial Analytics on a Reconfigurable System-on-Chip. Proceedings of the 11th International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies.https://doi.org/10.1145/3468044.3468056 

Ghasemzadeh Mohammadi, H., Jentzsch, F., Kuschel, M., Arshad, R., Rautmare, S., Manjunatha, S., Platzner, M., Boschmann, A., &; Schollbach, D. (2021). FLight: FPGA Acceleration of Lightweight DNN Model Inference in Industrial Analytics. https://doi.org/10.1007/978-3-030-93736-2_27__Machine Learning and Principles and Practice of Knowledge Discovery in Databases.https://doi.org/10.1007/978-3-030-93736-2_27