Convolutional Neural Networks (CNNs) are currently the widely adopted approach for computer vision tasks. A rapidly growing use case scenario is represented by IoT devices embedded in battery-powered systems, such as autonomous vehicles, where low power scheduling and optimization of the memory usage is recommended due to the high communication demands of convolutional layers. However, selecting an energy-efficient scheduling for a CNN is challenging and requires an extensive search of loop schedules. To tackle this problem, a design space exploration framework was developed to optimize CNN networks proposed in literature, which provides communication schedules and memory statistics such as energy and bandwidth usage.