Soft Error Reliability Assessment of ML Inference Models Executing on Resource-Constrained IoT Edge Devices
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-18599-1_6
Reference17 articles.
1. Abich, G., Gava, J., Reis, R., Ost, L.: Soft error reliability assessment of neural networks on resource-constrained IoT devices. In: IEEE International Conference on Electronics, Circuits and Systems (ICECS), pp. 1–4 (2020). https://doi.org/10.1109/ICECS49266.2020.9294951
2. Abich, G., Reis, R., Ost, L.: The impact of precision bitwidth on the soft error reliability of the mobilenet network. In: IEEE Latin American Symposium on Circuits and Systems (LASCAS), pp. 1–4 (2020). https://doi.org/10.1109/LASCAS51355.2021.9667153
3. Abich, G., Gava, J., Garibotti, R., Reis, R., Ost, L.: Impact of thread parallelism on the soft error reliability of convolution neural networks. In: IEEE Latin American Symposium on Circuits and Systems (LASCAS), pp. 1–4 (2022). https://doi.org/10.1109/LASCAS53948.2022.9789088
4. Abich, G., Gava, J., Garibotti, R., Reis, R., Ost, L.: Applying lightweight soft error mitigation techniques to embedded mixed precision deep neural networks. IEEE Trans. Circuits Syst. I: Regul. Pap. 68(11), 4772–4782 (2021). https://doi.org/10.1109/TCSI.2021.3097981
5. Abich, G., Garibotti, R., Reis, R., Ost, L.: The impact of soft errors in memory units of edge devices executing convolutional neural networks. IEEE Trans. Circuits Syst. II: Express Briefs 69(3), 679–683 (2022). https://doi.org/10.1109/TCSII.2022.3141243
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