smaller are black). Dashed red line: best accuracy when the binarized neural network is trained on CIFAR-10 classification task with full color images. 2000 training epochs were used. ]{} \[fig:accuracy\_cifar\_bin\]]{}

(topskip=0pt, botskip=0pt, midskip=0pt) \[scale = 0.35\][cifar10\_sto\_classifier.png]{} [[Accuracy on the CIFAR-10 classification task, but the stochastic computing approach is implemented at the end of the convolutional layers. Navy blue curve: training of the neural network in a conventional fashion. Light blue curve: classifier part of the neural network retrained with stochastic versions of the output of the convolutional layers. Dashed red line: best accuracy when the binarized neural network is trained on CIFAR-10 classification task with full color images. 2000 training epochs were used.]{} \[fig:accuracy\_cifar\_bin\_classifier\]]{}

[ We now apply this strategy to the more advanced CIFAR-10 dataset. We use a convolutional neural network with six convolutional layers, with kernel size of three by three and a stride of one (number of filters 384, 384, 384, 768, 768 and 1536) and three fully connected layers (number of neurons 1024, 1024 and 10). Training is done in the same conditions as the Fashion-MNIST case, using dropout and Adam optimizer, and the pytorch deep learning framework. In the stochastic computing BNN, CIFAR-10 images are 