Convolutional neural networks (CNNs) are progressively deployed on embedded systems, which is challenging because their computational and energy requirements need to be satisfied by devices with limited resources and power supplies. For instance, they can be implemented in the Internet of Things or edge computing, i.e., in applications using low-power and low-performance microcontroller units (MCUs). Monocore MCUs are not tailored to respond to the computational and energy requirements of CNNs due to their limited resources, but a multicore MCU can overcome these limitations. This paper presents an empirical study analysing three algorithms for scheduling CNNs on embedded systems at two different levels (neuron and layer levels) and evaluates their performance in terms of makespan and energy consumption using six neural networks, both in general and in the case of CubeSats. The results show that the SNN algorithm outperforms the other two algorithms (STD and STS) and that scheduling at the layer level significantly reduces the energy consumption. Therefore, embedded systems based on multicore MCUs are suitable for executing CNNs, and they can be used, for example, on board small satellites called CubeSats.