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investigates a radically new paradigm for Embedded AI: enabling devices to dynamically compress and adapt neural networks directly on-device after deployment. Inspired by how humans continuously optimize
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. This intervention constitutes optimal timing and a comprehensive design to achieve its intended goals, i.e., improved functional recovery post-TJA and general health, and decreased socio-economic burden
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optimally combined to deliver models with extremely constrained compute and memory footprints without compromising performance. This includes training spiking neural networks with multiple plasticities
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Belgium. The overall objective of FLEX-SMR is to develop cost-optimal solutions for integrating SMRs into industrial energy systems. In particular, the project investigates how an SMR can be operated