the other hand, with few stochastic presentation (one to five), the adapted input training technique allows reaching a quite high accuracy. If a single presentation is used at inference time, the network test accuracy is $86\%$. This test accuracy is equivalent to the one obtained when training a BNN with non-stochastic black and white versions of the Fashion-MNIST dataset (dashed black line in Fig. \[fig:accuracy\]). If three image presentation are used, the network test accuracy increases to [$88.7\%$]{}.

These results show that when using the stochastic computing version of BNN, the adapted training procedure should be used.

Choice of the Accumulation Layer for Stochastic Samples {#subsec:accumul}
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(topskip=0pt, botskip=0pt, midskip=0pt) \[scale = 0.35\][acc.png]{} [Accuracy on the Fashion MINIST classification task as function of the number of stochastic image presentation presented [ when the popcount value ias accumulated at different level of the network. The training was done with grayscale images and ]{}300 training epochs were used. \[fig:accumulation\] ]{}

Until now, at inference time, we have accumulated the outputs of the first layer over several presentations of the same image, then propagated the binarized output of the first layer to the other layers. An alternative strategy can be to perform the 