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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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/C++, and experience with deep learning frameworks such as PyTorch or TensorFlow Interest in hardware aware AI, including model compression, quantization, pruning, or efficient neural architectures
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