The automated resolution of cognitive tasks primarily relies on learning algorithms applied to neural networks which, when executed on standard CMOS based digital architectures, lead to a power consumption several orders of magnitude larger than what the brain would require. Moreover, Conventional Edge neural network solutions can only provide output predictions, lacking the ability to accurately convey prediction uncertainty due to their deterministic parameters and neuron activations, resulting in overconfident predictions. Being able to model and compute the uncertainty of a given prediction allows the user to make better decisions (e.g. in classification or decision making processes) that can be therefore explained, which is crucial in a variety of applications, such as the safety-critical tasks (e.g., autonomous vehicles, medical diagnosis and treatment, industrial robotics, and financial systems). Probabilistic neural network is a possible solution to deal with uncertainty prediction. In addition, the power consumption can be drastically reduced by using hardware computing systems with architectures inspired by biological or physical models. They are mainly based on nanodevices mimicking the properties of neurons such as the emission of stochastic or synchronous spikes. Numerous theoretical proposals have shown that nanoscale spintronic devices (MTJ) are particularly well adapted. They can be used as stochastic components or as deterministic components.