The computing solutions of tomorrow must be more energy efficient than those of today, which requires combined efforts to be conducted on multiple research areas: from new transistor technologies to innovative software algorithms by way of original processor architectures. This project enters into this last research area by revisiting the vector processing model, which provides a highly efficient way of exploiting data parallelism in scientific computations, sensor processing, and machine learning algorithms.
This collaborative research project between prof. Benoit Gosselin and two major industrial partners in advanced manufacturing and medical technologies aims to design, manufacture and test a tiny, inexpensive and easy-to-use wearable multi-sensing device to continuously monitor patients remotely, and help greatly to manage COVID-19.
The result of this project (which will be demonstrated by a use case) can make health equipments to be used outside of hospitals. This is achieved by reducing the computation cost of running Deep Learning models by 3rd party tools and use our accelerator solution to run the size reduced and optimized model. This greatly helps to lower the barrier for using costly equipments and make them more affordable and reachable to people in need of these equipments.