approach is therefore the most promising for Internet-of-Things or sensor networks applications, where relatively small neural networks can provide sufficient intelligence, but circuit cost and energy consumption are the most critical issues. [On deep neural networks, nevertheless, the approach of implementing only the classifier with a stochastic BNN, as mentioned in section \[subsec:CIFAR10\], can be of high interest.]{}

Conclusion
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In this work, we presented a stochastic computing approach to Binarized Neural Networks. This allows implementing them in an entirely binarized fashion, whereas in conventional BNNs, the first layer is not binary. We showed that the stochastic computing approach can reach recognition results similar to the conventional approach. We identified that for highest accuracy, the neural network should not be trained with [regular]{} images as conventional BNNs: it it is more beneficial to train stochastic BNNs with stochastic binarized images, using the same number of image presentation as will be used during inference. [ The design of a full BNN ASIC relying on in-memory computing, then highlighted the benefits of BNNs in terms of area and energy consumption.]{} Stochastic BNNs allow using the same compact architecture for all layers, which leads to strong benefits in terms of area ($62\%$ reduction 