Satellite communication (SatCom) systems operations centers currently require high human intervention, which leads to increased operational expenditure (OPEX) and implicit latency in human action that causes degradation in the quality of service (QoS). Consequently, new SatCom systems leverage artificial intelligence and machine learning (AI/ML) to provide higher levels of autonomy and control. Onboard processing for advanced AI/ML algorithms, especially deep learning algorithms, requires an improvement of several magnitudes in computing power compared to what is available with legacy, radiation-tolerant, space-grade processors in space vehicles today. The next generation of onboard AI/ML space processors will likely include a diverse landscape of heterogeneous systems. This manuscript identifies the key requirements for onboard AI/ML processing, defines a reference architecture, evaluates different use case scenarios, and assesses the hardware landscape for current and next-generation space AI processors.

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This work presents the development of self-modifiable Intellectual Property (IP) modules for histogram calculation using the modelbased design technique provided by Xilinx System Generator. In this work, an analysis and a comparison among histogram calculation architectures are presented, selecting the best solution for the design flow used. Also, the paper emphasizes the use of generic architectures capable of been adjustable by a self configurable procedure to ensure a processing flow adequate to the application requirements. In addition, the implementation of a configurable IP module for histogram calculation using a model-based design flow is described and some implementation results are shown over a Xilinx FPGA Spartan-6 LX45.

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