Diagnostic Data Integration Using Deep Neural Networks for Real-Time Plasma Analysis

Recent advances in acquisition equipment are providing experiments with growing amounts of precise, yet affordable sensors. At the same time, an improved computational power, coming from new hardware resources [GPU, field-programmable gate array (FPGA), adaptive compute acceleration platform (ACAP)] has been made available at relatively low costs. This led us to explore the possibility of completely renewing the chain of acquisition for a fusion experiment, where many high-rate sources of data, coming from different diagnostics, can be combined in a wide framework of algorithms. If, on the one hand, adding new data sources with different diagnostics enriches our knowledge about physical aspects, on the other hand, the dimensions of the overall model grow, making relations among variables more and more opaque. A new approach for integrating such heterogeneous diagnostics, based on the composition of deep variational autoencoders, could ease this problem, acting as a structural sparse regularizer. This has been applied to RFX-mod experimental data, integrating the soft X-ray linear images of plasma temperature with the magnetic state. However, to ensure a real-time signal analysis, these algorithmic techniques must be adapted to run in well-suited hardware. In particular, it is shown that, attempting a quantization of neuron transfer functions, such models can be adapted to run in an embedded programmable logic device. The resulting firmware, approximating the deep inference model to a set of simple operations, fits well with the simple logic units that are largely abundant in FPGAs. This is the key factor that permits the use of affordable hardware with complex deep neural topology and operates them in real-time.