Development Of Deep Learning Applications In Fpga-Based Fusion Diagnostics Using Irio-Opencl And Nds

Many of the signals that are relevant to fusion science come from 1D signals or time-series. In this field, the resulting Neural Networks are much simpler than the more mainstream vision-based neural networks. A significant reduction in both dimension and complexity make them suitable to be synthesized in FPGAs. We have developed new features for the IRIO-OpenCL platform to support this technology for fusion problems. The work presented analyzes the feasibility of such diagnostics use cases and how they can be integrated with the help of OpenCL technology. The development and testing platform consists of an MTCA.4 system with an AMC module integrating an Intel Arria 10 FPGA. An ADC connected using the FMC interface samples the analog signals passed to the OpenCL processing kernels. By using OpenCL, the FPGA kernels communicate with the host machine in a standardized way. This brings two main advantages. First, this is an ideal prototyping framework. Second, once a solution is final, the FPGA kernels are integrated into the control system (EPICS) using the IRIO-OpenCL layer, which has been developed with Nominal Device Support (NDSv3). Finally, we present the results of the optimizations to the kernels of an application example based on a neutron/gamma discrimination Neural Network, which achieves up to a classification rate of 1.3 MEvents/s.