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Quantization techniques are pivotal in reducing the memory and computationaldemands of deep neural network inference. Existing solutions, such asZeroQuant, offer dynamic quantization for models like BERT and GPT but overlookcrucial memory-bounded operators and the complexities of per-tokenquantization. Addressing these gaps, we present a novel, fullyhardware-enhanced robust optimized post-training W8A8 quantization framework,ZeroQuant-HERO. This framework uniquely integrates both memory bandwidth andcompute-intensive operators, aiming for optimal hardware performance.Additionally, it offers flexibility by allowing specific INT8 modules to switchto FP16/BF16 mode, enhancing accuracy.

ZeroQuant-HERO: Hardware-Enhanced Robust Optimized Post-Training Quantization Framework for W8A8 Transformers