Pfenning S, Simpetru R, Pollak N, Del Vecchio A, Fey D (2024)
Publication Language: English
Publication Type: Journal article, Original article
Publication year: 2024
Book Volume: 19
Pages Range: 1-14
Journal Issue: 1
URI: http://www.iadisportal.org/ijcsis/papers/2024190101.pdf
DOI: 10.33965/ijcsis_2024190101
Open Access Link: http://www.iadisportal.org/ijcsis/papers/2024190101.pdf
The advancements in deep neural network design have led to a significant increase in the possibilities and functioning of AI-assisted medical hardware. To make use of this progress in the field of mobile applications or even as wearable devices, a suitable hardware-software ecosystem must be identified to meet the high computation and memory demands of neural networks with minimal energy consumption. In this paper, we analyze an up-to-date heterogenous embedded platform employing a deep convolutional network for hand position recognition through electromyography signals. Our evaluation aimed to determine the optimization efforts required for the architecture to function as a human wearable device and identify the most suitable accelerators on the given platform for this task.
APA:
Pfenning, S., Simpetru, R., Pollak, N., Del Vecchio, A., & Fey, D. (2024). ANALYSIS OF EMBEDDED GPU ARCHITECTURES FOR AI IN NEUROMUSCULAR APPLICATIONS. IADIS International Journal on Computer Science and Information Systems, 19(1), 1-14. https://doi.org/10.33965/ijcsis_2024190101
MLA:
Pfenning, Simon, et al. "ANALYSIS OF EMBEDDED GPU ARCHITECTURES FOR AI IN NEUROMUSCULAR APPLICATIONS." IADIS International Journal on Computer Science and Information Systems 19.1 (2024): 1-14.
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