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GRAU: A Generic Runtime-Reconfigurable Activation-Requantization Unit for Quantized Neural Network Accelerators
As neural networks continue to scale, low-precision quantization is widely adopted in edge accelerators, but conventional multi-threshold activation hardware requires 2^n-1 thresholds for n-bit integer outputs, leading to rapidly increasing hardware costs. We propose GRAU, a Generic Runtime-Reconfig
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