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Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Henwood, S. (2024). Optimisation de réseaux de neurones profonds pour déploiement sur matériel incertain à faible consommation d'énergie [Ph.D. thesis, Polytechnique Montréal]. Available
Henwood, S., Torcato Mordido, G. F., Savaria, Y., Anbil Parthipan, S. C., & Leduc-Primeau, F. (2024, October). Sharpness-Aware Minimization Scaled by Outlier Normalization for Robust DNNs on In-Memory Computing Accelerators [Paper]. 58th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA. External link
Henwood, S., Savaria, Y., & Leduc-Primeau, F. (2024, November). MemNAS: Super-net Neural Architecture Search for Memristor-based DNN Accelerators [Paper]. IEEE Workshop on Signal Processing Systems (SiPS 2024), Cambridge, MA, USA (6 pages). External link
Henwood, S., Leduc-Primeau, F., & Savaria, Y. (2020, August). Layerwise noise maximisation to train low-energy deep neural networks [Paper]. 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS 2020), Genova, Italy. External link
Henwood, S. (2019). Algorithme de l'alpiniste pour l'étude de cartes de contrôle en coordonnées parallèles [Master's thesis, Polytechnique Montréal]. Available
Kern, J., Henwood, S., Torcato Mordido, G. F., Dupraz, E., Aissa-El-Bey, A., Savaria, Y., & Leduc-Primeau, F. (2024). Fast and Accurate Output Error Estimation for Memristor-Based Deep Neural Networks. IEEE Transactions on Signal Processing, 72, 1205-1218. External link
Kern, J., Henwood, S., Torcato Mordido, G. F., Dupraz, E., Aissa-El-Bey, A., Savaria, Y., & Leduc-Primeau, F. (2022, June). MemSE: Fast MSE Prediction for Noisy Memristor-Based DNN Accelerators [Paper]. IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) - Intelligent Technology in the Post-Pandemic Era, Incheon, South Korea. External link
Tamrin, M. O., Henwood, S., Dubois, J.-F., Brault, J.-J., Chidami, S., & Bassetto, S. (2019, June). Using deep learning approaches to overcome limited dataset issues within semiconductor domain [Paper]. 17th IEEE International New Circuits and Systems Conference (NEWCAS 2019), Munich, Germany (4 pages). External link
