dCSR: A Memory-Efficient Sparse Matrix Representation for Parallel Neural Network Inference. - Elias Trommer; Bernd Waschneck; Akash Kumar 001 (ICCAD, 2021)

Combining Gradients and Probabilities for Heterogeneous Approximation of Neural Networks. - Elias Trommer; Bernd Waschneck; Akash Kumar 001 (ICCAD, 2022)

High-Throughput Approximate Multiplication Models in PyTorch. - Elias Trommer; Bernd Waschneck; Akash Kumar 001 (DDECS, 2023)

QoS-Nets: Adaptive Approximate Neural Network Inference. - Elias Trommer; Bernd Waschneck; Akash Kumar 001 (CoRR, 2024)

Smaller Together: Groupwise Encoding of Sparse Neural Networks. - Elias Trommer; Bernd Waschneck; Akash Kumar 001 (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2024)

Fast Retraining of Approximate CNNs for High Accuracy. - Elias Trommer; Bernd Waschneck; Akash Kumar 001 (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2025)