Compiler Toolchains for Deep Learning Workloads on Embedded Platforms. - Max Sponner; Bernd Waschneck; Akash Kumar 001 (CoRR, 2021)

Temporal Patience: Efficient Adaptive Deep Learning for Embedded Radar Data Processing. - Max Sponner; Julius Ott; Lorenzo Servadei; Bernd Waschneck; Robert Wille; Akash Kumar 001 (CoRR, 2023)

Harnessing Temporal Information for Efficient Edge AI. - Max Sponner; Lorenzo Servadei; Bernd Waschneck; Robert Wille; Akash Kumar 001 (FMEC, 2024)

Efficient Post-training Augmentation for Adaptive Inference in Heterogeneous and Distributed IoT Environments. - Max Sponner; Lorenzo Servadei; Bernd Waschneck; Robert Wille; Akash Kumar 001 (SAMOS, 2024)

Leveraging Temporal Patterns: Automated Augmentation to Create Temporal Early Exit Networks for Efficient Edge AI. - Max Sponner; Lorenzo Servadei; Bernd Waschneck; Robert Wille; Akash Kumar 001 (IEEE Access, 2024)

Temporal Decisions: Leveraging Temporal Correlation for Efficient Decisions in Early Exit Neural Networks. - Max Sponner; Lorenzo Servadei; Bernd Waschneck; Robert Wille; Akash Kumar 001 (CoRR, 2024)

Adapting Neural Networks at Runtime: Current Trends in At-Runtime Optimizations for Deep Learning. - Max Sponner; Bernd Waschneck; Akash Kumar 001 (ACM Comput. Surv., 2024)