presented with binarized channel: each RGB channel pixel presents a value of zero or one. This value is chosen randomly with a probability equal to the RGB value of the corresponding pixel of the image. Accumulation of stochastic realization is realized at the first layer, as described in section \[subsec:accumul\]. ]{}

[ Fig. \[fig:accuracy\_cifar\_bin\] shows that the results on CIFAR-10 are very similar to the ones on Fashion-MNIST (Fig. \[fig:accuracy\]). It present results obtained using the weights trained with full color images, and weights obtained with the adapted training approach. In both cases, the stochastic BNN results approach regular BNN results when the number of presentation $T$ of stochastic images is increased. The adapted training nevertheless gives highly superior results and should be preferred. This highlights that the stochastic BNN approach can be applicable to more complicated tasks than Fashion-MNIST. ]{}

[ We now consider a variation of this scheme, a partially binarized neural network. Fully connected layers of neural networks are particularly adapted for in-memory BNN implementation [@yu2018neuro; @bocquet2018memory], as these layers involve large quantities of memories. Convolutional layers are less memory intensive, and thus benefit less from binarization, while requiring increasing the number of channels when binarized [@courbariaux2016binarized]. 