CODAI

Publications

2023

Matheus Fellype Ferraz, Birte Kristina Friesel, Olaf Spinczyk
Pros and Cons of Executable Neural Networks for Deeply Embedded Systems. CODAI (2023)
Bernhard Vogginger, Florian Kelber, Shambhavi Balamuthu Sampath, Johannes Partzsch, Christian Mayr
Performance models and energy-optimal scheduling of DNNs on many-core hardware with dynamic power management. CODAI (2023)
Lotfi Abdelkrim Mecharbat, Hadjer Benmeziane, Hamza Ouarnoughi, Smaïl Niar
HyT-NAS: Hybrid Transformers Neural Architecture Search for Edge Devices. CODAI (2023)
Philipp van Kempen, Rafael Stahl, Daniel Mueller-Gritschneder, Ulf Schlichtmann
MLonMCU: TinyML Benchmarking with Fast Retargeting. CODAI (2023)
Chen Liu, Matthias Jobst, Liyuan Guo, Xinyue Shi, Johannes Partzsch, Christian Mayr
Deploying Machine Learning Models to Ahead-of-Time Runtime on Edge Using MicroTVM. CODAI (2023)
Federico Nicolás Peccia, Oliver Bringmann
Integration of a systolic array based hardware accelerator into a DNN operator auto-tuning framework. CODAI (2023)
Proceedings of the 2023 Workshop on Compilers, Deployment, and Tooling for Edge AI, CODAI 2023, Hamburg, Germany, 21 September 2023 CODAI (2023)
Samira Ahmadifarsani, Rafael Stahl, Philipp van Kempen, Daniel Mueller-Gritschneder, Ulf Schlichtmann
Towards Rapid Exploration of Heterogeneous TinyML Systems using Virtual Platforms and TVM's UMA. CODAI (2023)
Yao Lu, Hiram Rayo Torres Rodriguez, Sebastian Vogel, Nick van de Waterlaat, Pavol Jancura
Scaling Up Quantization-Aware Neural Architecture Search for Efficient Deep Learning on the Edge. CODAI (2023)
Endri Bezati
ART: An Actor transition systems RunTime for enabling efficient partitioning of neural network graphs. CODAI (2023)