  Current artificial neural networks are trained with parameters encoded as
floating point numbers that occupy lots of memory space at inference time. Due
to the increase in the size of deep learning models, it is becoming very
difficult to consider training and using artificial neural networks on edge
devices. Binary neural networks promise to reduce the size of deep neural
network models, as well as to increase inference speed while decreasing energy
consumption. Thus, they may allow the deployment of more powerful models on
edge devices. However, binary neural networks are still proven to be difficult
to train using the backpropagation-based gradient descent scheme. This paper
extends the work of \cite{crulis2023alternatives}, which proposed adapting to
binary neural networks two promising alternatives to backpropagation originally
designed for continuous neural networks, and experimented with them on simple
image classification datasets. This paper proposes new experiments on the
ImageNette dataset, compares three different model architectures for image
classification, and adds two additional alternatives to backpropagation.
