Cellular Neural Networks (CNN) can be embodied in the form of a focal-plane image processor. They represent a computing paradigm with evident advantages in terms of energy and resources. Their operation relies in the strong parallelization of the processing chain thanks to a distributed allocation of computing resources. In this way, image sensing and ultra-fast processing can be embedded in a single chip. This makes them good candidates for portable and/or distributed applications in fields like autonomous robots or smart cities. With the irruption of visual features learning through convolutional neural networks (ConvNets), several works attempt to implement this functionality within the CNN framework. In this paper we carry out some experiments on the implementation of ConvNets with CNN hardware in the form of a focal-plane image processor. It is shown that ultra-fast inference can be implemented, using as an example a LeNet-based ConvNet architecture.