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the lifetime of the device. Consequently, energy consumption, memory usage, and computational requirements remain fixed, even when the application or environmental conditions evolve over time. This project
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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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deploying the face de‑identification pipeline on resource‑constrained hardware, optimizing the underlying AI models based on latency, memory, and power constraints, and building demonstrators that showcase
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