Frank Mokadem

Feb9 2023, 11:30 am, EC4-2101A

*** 11:30am Presenter:  Frank Mokadem ***

Summary: Please join me Friday as I present my research on reduce the memory footprint of CNNs through tensor representation and factorization. I combine approximation theory from linear algebra and grid search to find lighter representations of networks. This method, already proven to reduce memory footprint with at least 1 order of magnitude, can be improved to minimize loss in predictive power of the network. My hope is to reduce big models into deployable lighter models able to function on edge devices, furthermore, I hope to automate this process and expand it to more CNN architectures.