Pfenning S, Simpetru R, Pollak N, Del Vecchio A, Fey D (2023)
Publication Type: Conference contribution
Publication year: 2023
Publisher: IADIS Press
Pages Range: 39-46
Conference Proceedings Title: International Conferences on Applied Computing 2023, AC 2023 and WWW/Internet 2023, ICWI 2023
Event location: Funchal, Madeira Island, PRT
The recent progress in the design of deep neural networks has led a huge step forward in inference accuracy and opened new opportunities also in the design of AI aided medical hardware. However deep neural models have a high demand in both computational as well as memory resources. To make these systems applicable in the field, it is necessary to find a suitable hardware-software ecosystem, which can meet these demands at a sufficiently low energy consumption. For this purpose, we evaluated a modern heterogeneous embedded platform with a deep convolutional network for hand position recognition, based on the collection of electromyography signals. The goal of our evaluation was to find out, how much effort must be spent for optimization, if the architecture itself would be sufficient for the usage as human wearable device and which of the available accelerators on the embedded platform are best suited for the task.
APA:
Pfenning, S., Simpetru, R., Pollak, N., Del Vecchio, A., & Fey, D. (2023). EVALUATION OF GPU-CENTRIC EMBEDDED ARCHITECTURES IN THE FIELD OF NEUROMUSCULAR EMG-BASED AI. In International Conferences on Applied Computing 2023, AC 2023 and WWW/Internet 2023, ICWI 2023 (pp. 39-46). Funchal, Madeira Island, PRT: IADIS Press.
MLA:
Pfenning, Simon, et al. "EVALUATION OF GPU-CENTRIC EMBEDDED ARCHITECTURES IN THE FIELD OF NEUROMUSCULAR EMG-BASED AI." Proceedings of the 20th International Conference Applied Computing, AC 2023 and the 22nd International Conference WWW/Internet, ICWI 2023, Funchal, Madeira Island, PRT IADIS Press, 2023. 39-46.
BibTeX: Download