StatAssist & GradBoost: A Study on Optimal INT8 Quantization-aware Training from Scratch

17 Jun 2020Taehoon KimYoungjoon YooJihoon Yang

This paper studies the scratch training of quantization-aware training (QAT), which has been applied to the lossless conversion of lower-bit, especially for INT8 quantization. Due to its training instability, QAT have required a full-precision (FP) pre-trained weight for fine-tuning and the performance is bound to the original FP model with floating-point computations... (read more)

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