HERMES (High Energy Rapid Modular Ensemble of Satellites)Technological and Scientific pathfinder is a space borne mission based on a LEO constellation of nano-satellites. The 3U CubeSat buses host new miniaturized detectors to probe the temporal emission of bright high-energy transients such as Gamma-Ray Bursts (GRBs). Fast transient localization, in
The HERMES-TP/SP (High Energy Rapid Modular Ensemble of Satellites -Technologic and Scientific Pathfinder) is an in-orbit demonstration of the so-called distributed astronomy concept. Conceived as a mini-constellation of six 3U nanosatellites hosting a new miniaturized detector, HERMES-TP/SP aims at the detection and accurate localisation of bright high-energy transients such as Gamma-Ray Bursts. The large energy band, the excellent temporal resolution and the wide field of view that characterize the detectors of the constellation represent the key features for the next generation highenergy all-sky monitor with good localisation capabilities that will play a pivotal role in the future of Multi-messenger Astronomy. In this work, we will describe in detail the temporal techniques that allow the localisation of bright transient events taking advantage of their almost simultaneous observation by spatially spaced detectors. Moreover, we will quantitatively discuss the all-sky monitor capabilities of the HERMES Pathfinder as well as its achievable accuracies on the localisation of the detected Gamma-Ray Bursts.
The growing numbers of application areas for artificial intelligence (AI) methods have led to an explosion in availability of domain-specific accelerators, which struggle to support every new machine learning (ML) algorithm advancement, clearly highlighting the need for a tool to quickly and automatically transition from algorithm definition to hardware implementation and explore the design space along a variety of SWaP (size, weight and Power) metrics. The software defined architectures (SODA) synthesizer implements a modular compilerbased infrastructure for the end-to-end generation of machine learning accelerators, from high-level frameworks to hardware description language. Neuromorphic computing, mimicking how the brain operates, promises to perform artificial intelligence tasks at efficiencies ordersof-magnitude higher than the current conventional tensor-processing based accelerators, as demonstrated by a variety of specialized designs leveraging Spiking Neural Networks (SNNs). Nevertheless, the mapping of an artificial neural network (ANN) to solutions supporting SNNs is still a non-trivial and very device-specific task, and completely lacks the possibility to design hybrid systems that integrate conventional and spiking neural models. In this paper, we discuss the design of such an integrated generator, leveraging the SODA Synthesizer framework and its modular structure. In particular, we present a new MLIR dialect in the SODA frontend that allows expressing spiking neural network concepts (e.g., spiking sequences, transformation, and manipulation) and we discuss how to enable the mapping of spiking neurons to the related specialized hardware (which could be generated through middle-end and backend layers of the SODA Synthesizer). We then discuss the opportunities for further integration offered by the hardware compilation infrastructure, providing a path towards the generation of complex hybrid artificial intelligence systems.
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