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Tampere University
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Naive distributed inference on edge devices can be *slower* than local execution due to CPU-GPU communication bottlenecks, but a profiling-driven adaptive approach can flip the script for significant gains.
Explicit dropout achieves superior performance without the randomness of traditional methods, offering a clearer path to regularization control in Transformer models.
