A scalable CNN architecture and its application to short exposure stellar images processing on a HPRC

A CNN-based algorithm for short exposure image processing and an application-specific computing architecture developed to accelerate its execution are presented. Algorithm is based on a flexible and scalable Cellular Neural Networks (CNN) architecture specifically designed to optimize the projection of CNN kernels on a programmable circuit. The objective of the proposed algorithm is to minimize the adverse effect that atmospheric disturbance has on the images obtained by terrestrial telescopes. Algorithm main features are that it can be adapted to the detection of several astronomical objects and it supports multi-stellar images. The implementation platform made use of a High Performance Reconfigurable Computer (HPRC) combining general purpose standard microprocessors with custom hardware accelerators based on FPGAs, to speed up execution time. The hardware/software partitioning and co-design process have been carried out using high level design tools, instead of traditional Hardware Description Languages (HDLs). Results are presented in terms of circuit area/speed, processing performance and output quality.