Progetti di ricerca

Space missions increasingly require autonomous and efficient on-board data processing to overcome challenges such as limited communication bandwidth, high latency, and the need for real-time decision-making. While traditional artificial intelligence (AI) algorithms have demonstrated remarkable performance in these tasks, their… Leggi tutto high power consumption and computational demands make them less suitable for resource-constrained environments like space [6]. This proposal aims to advance satellite edge computing by developing a neuromorphic processing module that interfaces a RISC-V core with Spiking Neural Networks (SNNs). Inspired by the human brain's efficiency and adaptability, SNNs process information through intermittent spikes, activating neurons only when inputs exceed a threshold. This event-driven approach drastically reduces power consumption, specifically for continuous data streams, while maintaining high computational efficiency, making SNNs suitable for space applications [1][6]. Unlike conventional deep learning models, such as Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs), SNNs encode information in a time-dependent manner, enhancing robustness against radiation-induced errors—a critical requirement for space systems [2]. This activity will focus on adapting an existing RISC-V core to interface with a custom-designed SNN. Thanks to the high degree of customization offered by the RISC-V architecture and its open-source nature, I aim to develop an integrated module that combines a traditional processor with an SNN-based accelerator. To enable efficient control and communication between the processor and the SNN, the project will also explore the development of custom instructions for specific SNN tasks. The module will be validated through a practical use case to assess its performance in terms of computational efficiency and radiation resistance, ensuring its suitability for deployment in the challenging conditions of space.

Atmospheric mineral dust (hereby ’dust’) is a key component of the Earth system and has widespread climatic impacts. Dust affects the global climate and environment through, for example, radiative forcing, cloud formation, and nutrient cycles1,2, but its parametrisation in global climate and… Leggi tutto Earth system models is defective3-5. Recently, the current net effect of dust direct radiative forcing on global climate was calculated as most likely negative, although potentially slightly positive given the large uncertainty on this estimate (-0.2 ± 0.5 W m-2 with 90% confidence interval5 ). Many uncertainties remain in such calculations, largely caused by incomplete data on the chemical and physical properties of dust particles, and on the meteorological processes and driving mechanisms behind dust emission, transport, and deposition (‘dust cycle’). While climate models have become increasingly complex, the parametrisation of dust processes, although also improving, lags behind this development, and its uncertainties amplify as models get more sophisticated4 . This limits our understanding on the Earth system as a whole and hampers the ability of models to predict the effects of climate changes on the natural world and societies. A necessary first step to enhance models is to have more observational data on dust properties. The arid and semi-arid East Asia is one of the key global dust sources, but, for example, observational studies on East Asian dust mineralogy were ~50% less than those from the northern African region by 20156 . Compared to other major global dust sources, East Asia is unique in that it also hosts a globally exceptional geologic archive of windblown dust from the past ~25 million years, the Chinese Loess Plateau (CLP; Fig. 1). Extensive research, especially provenance studies, on the CLP dust deposits has been a key in revealing the long-term, two-way link between dust and climate changes in the Earth’s history7-10 . Dust provenance analysis is, in fact, one of the few methods that simultaneously provide information on all the steps of the dust cycle (emission, transport, deposition). Because of the scarcity of interdisciplinary studies involving both atmospheric scientists and geologists, the CLP region has much unused potential to establish a link between past and present dust activity to better understand both and to enhance the modelling aspects of dust11-13 . In this project, I study the properties and provenance of dust collected by active and passive samplers in East Asia during 2019–2023.