consumption of the full Fashion-MNIST classifier systems, for the classification of one image. Light blue: stochastic fully binarized binary architecture. Navy blue: Conventional BNN with non binary (8 bit fixed point) first layers. The neural networks have two layers with 1024 neurons each. The light blue area indicate the regime where the non-binary first layer is more energy efficient thant the fully binarized system.\[fig:energy\] ]{}

[The savings in terms of area transfer directly at the system level. We now consider the whole neural network used for Fashion-MNIST classification throughout section \[sec:network\].]{} Using our architecture, a full BNN with eight bit first layer occupies $1.95\ mm^2$, while the BNN with stochastic binarized first layer occupies $0.73\ mm^2$, a $62\%$ saving in area. These area values were extracted from a system designed for a $T$ value of eight.

Fig. \[fig:energy\] plots the energy consumption for recognizing an image with our ASIC architecture, as a function of the number of presented stochastic images. This is compared with the energy cost of the same architecture, but using a non stochastic first layer, with eight bit input. We see that the system with stochastic first layer is more energy efficient than the system with non-binary 