 | Up a level |
Collaboration networkThis graph maps the connections between all the collaborators of {}'s publications listed on this page.
Each link represents a collaboration on the same publication. The thickness of the link represents the number of collaborations.
Use the mouse wheel or scroll gestures to zoom into the graph.
You can click on the nodes and links to highlight them and move the nodes by dragging them.
Hold down the "Ctrl" key or the "⌘" key while clicking on the nodes to open the list of this person's publications.
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.
Askari Hemmat, M., Wagner, S., Bilaniuk, O., Hariri, Y., Savaria, Y., & David, J. P. (2023, January). BARVINN: Arbitrary Precision DNN Accelerator Controlled by a RISC-V CPU [Paper]. 28th Asia and South Pacific Design Automation Conference (ASP-DAC 2023), Tokyo, Japan. External link
Askari Hemmat, M., Dupuis, T., Fournier, Y., El Zarif, N., Cavalcante, M., Perotti, M., Gurkaynak, F., Benini, L., Leduc-Primeau, F., Savaria, Y., & David, J. P. Quark: an integer RISC-V vector processor for sub-byte quantized DNN inference [Paper]. 2023 IEEE International Symposium on Circuits and Systems (ISCAS 2023), Monterey, CA, USA (5 pages). External link
Askari Hemmat, M., Bilaniuk, O., Wagner, S., Savaria, Y., & David, J. P. (2021, May). RISC-V barrel processor for deep neural network acceleration [Paper]. 53rd IEEE International Symposium on Circuits and Systems (ISCAS 2021), Daegu, Korea (5 pages). External link
Askari Hemmat, M., Bilaniuk, O., Wagner, S., Savaria, Y., & David, J. P. (2020, May). RISC-V Barrel Processor for Accelerator Control [Paper]. 28th Annual IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM 2020), Fayetteville, AR (1 page). External link
Askari Hemmat, M., Savaria, Y., David, J. P., Honari, S., Perone, C. S., Rouhier, L., & Cohen-Adad, J. (2019, October). U-Net Fixed-Point Quantization for Medical Image Segmentation [Paper]. 22nd International Conference on Medical Image Computing & Computer Assisted Intervention (MICCAI 2019), Shenzhen, China. External link
Askari Hemmat, M., Honari, S., Rouhier, L., Perone, C. S., Cohen-Adad, J., Savaria, Y., & David, J. P. (2019, October). U-net fixed-point quantization for medical image segmentation [Paper]. 1st International Workshop on Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention (HAL-MICCAI 2019), Shenzhen, China. External link
Dupuis, T., Fournier, Y., Askari Hemmat, M., El Zarif, N., Leduc-Primeau, F., David, J. P., & Savaria, Y. (2023, June). Sparq: A Custom RISC-V Vector Processor for Efficient Sub-Byte Quantized Inference [Paper]. 21st IEEE Interregional NEWCAS Conference (NEWCAS 2023), Edinburgh, United Kingdom (5 pages). External link
Humblet, E., Dupuis, T., Fournier, Y., Askari Hemmat, M., Leduc-Primeau, F., David, J. P., & Savaria, Y. (2024, August). MSPARQ: A RISC-V Vector Processor Array Optimized for Low-Resolution Neural Networks [Paper]. IEEE 67th International Midwest Symposium on Circuits and Systems (MWSCAS 2024), Springfield, MA, USA. External link
Sankaran, A., Mastropietro, O., Saboori, E., Idris, Y., Sawyer, D., Askari Hemmat, M., & Hacene, G. B. (2021, February). Deeplite Neutrino (TM): An End-to-End Framework for Constrained Deep Learning Model Optimization [Paper]. 35th AAAI Conference on Artificial Intelligence / 33rd Conference on Innovative Applications of Artificial Intelligence / 11th Symposium on Educational Advances in Artificial Intelligence. External link
