InfernoNet implements static INT8 post-training quantization on a custom CNN for fire detection, exporting quantized weights and dyadic requantization parameters for edge deployment on AVX-VNNI CPUs and FPGAs.
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2026-07-29
It bridges PyTorch FX graph quantization with bare-metal hardware verification by converting floating-point requantization into hardware-ready dyadic fractions, achieving 3.72x compression while preserving spatial features.
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