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Realizing Low-Precision Storage and Sparse Execution in a Folded FPGA Speech Enhancer
Hardware-aware training often optimizes FPGA resources, latency, or bit operations, but folding changes what compression saves: shared arithmetic decouples precision, weight storage, and scheduled work. We derive training objectives from the measured costs of a folded streaming speech-enhancement ac
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