## 2 Overview of the Issue  
One of the main issue affecting fusion experiments is related to the occurrence of disruptions [11] which are catastrophic events due to the loss of the plasma confinement in a very short time window of the order of milliseconds. Such events stresses the conductive walls, including the PFCs, with forces which magnitude grows non-linearly with the main plasma current [12]. At JET, considering a plasma current on axis of 3 MA, forces of about 3 MN, that are comparable to the weight of a F15, have been experienced. ITER is expected to operate at 15 MA, therefore, considering the non-linear dependence on the plasma current, unintentional disruptions could cause serious damages to the device. It is straightforward then to require that such occurrences have to be avoided, at least during high power discharges. Since their initial occurrence, scientists began studying how to mitigate the impact of a disruption on the main vessel, to prevent damages. Nowadays, reliable algorithms monitoring in real time the discharge, are available for mitigation purposes [13]. The next natural objective is represented by avoidance, as well as on the need of developing algorithms capable of detecting different chain of events leading to a disruption, to trigger therefore external interventions in order to correct the plasma disruptive path and guarantee the safe continuation of the discharge. Such approach is still an open issue, but it is evidently a necessary step to be achieved for stable and safe operations [14]. The best results achieved on disruptions have been obtained by following the nowadays well established and quite popular approach based on machine learning (ML) techniques [13]. In [PERSON] et al. [15] a systematic comparison of the most widespread and used algorithms is performed. A Multilayer Perceptron Neural Network (MLP-NN), a Generative Topographic Mapping (GTM) and a Convolutional Neural Networks (CNN) algorithm have been considered. Authors have demonstrated the capability of producing early warning times and, considering the MLP-NN and the CNN, a reduced number of false alarms. The GTM has been highlighted because of its capability of providing easier to interpret models w.r.t the other two, and it is shown how it can properly track a discharge from a not disruptive to a disruptive area. Authors have also highlighted the importance of bolometric data in their analysis. It has also to be stated in fact that radiative events play a very important role, since they can influence the initial phases and the growth of MHD instabilities [16] which then can lead to a disruption. What stated above then, stresses the importance of the work done by [PERSON] et al. [17], devoted to the acceleration of a Maximum Likelihood (ML) algorithm for bolometric tomographies in a ITER compatible environment. The ML algorithm has been implemented and validated on JET [18], and more recently on AUG [19] also. The code has the unique feature of providing the confidence intervals on its estimates and therefore, on the quantities that can be derived from the reconstructed emission patterns. Bolometric Tomography (BT) is an important tool in any fusion experiment indeed. Reliable estimates of specific quantities are used on a daily basis also as inputs of modelling codes such as TRANSP [20]. At the same time BT are relevant for power balance and transport studies, including impurity profiles estimates and impurity screening [21, 22]. [PERSON]'s et al. work have shown how their first released version of the accelerated algorithm, obtained using an ITER fast controller platform compatible with either Ubuntu 18.04 or the ITER Codac Core System distributions (6.1.2), is faster than a factor ten w.r.t the unoptimized MATLAB version, being therefore compatible for a JET intershot application. Improvements are expected to be achieved in a future release to reach the goal of 25 ms per iteration, compatible then for an ITER real-time implementation.