One of the many challenging tasks when deploying deep learning models on resource-constrained devices such as embedded systems, mobile phones, or IoT devices is meeting the tight memory requirements of these environments....
In today's world of high-performance computing, optimizing deep neural networks for embedded hardware is crucial for achieving real-time performance and meeting the demands of users and applications. One of the key factors in achieving optimal performance is reducing the execution time of these models....
