accumulation over the realizations of the input images at another layer. If the accumulation is done at the last layer, this procedure corresponds to using stochastic computing in the whole depth of the neural network.

Fig. \[fig:accumulation\] presents the test accuracy of the neural network on the Fashion-MNIST dataset, as a function of the number of presented realizations of the input images, for the different accumulation strategy, in networks trained with the adapted training strategy. [This Figure shows that that the different accumulation strategy lead to equivalent accuracy, consistently with the principles of stochastic computing. ]{} [The strategy of accumulation at the first layer is retained for the rest of the paper, as it allows for the minimum energy consumption.]{}

 [Extension to the CIFAR-10 Dataset]{}  {#subsec:CIFAR10}
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(topskip=0pt, botskip=0pt, midskip=0pt) \[scale = 0.35\][cifar10\_noEB.png]{} [[Accuracy on the CIFAR-10 classification task as function of the number of stochastic image presented for the two training methods. Navy blue curve: training of the neural network with color images. Light blue curve: training with presentation of stochastic binarized images. Dashed black line: accuracy when training with a binarized color image (i.e. RGB values with a value greater than 0.5 are white and pixels that are 