  In this work, we quantize a trained Transformer machine language translation
model leveraging INT8/VNNI instructions in the latest Intel$^\circledR$
Xeon$^\circledR$ Cascade Lake processors to improve inference performance while
maintaining less than 0.5$\%$ drop in accuracy. To the best of our knowledge,
this is the first attempt in the industry to quantize the Transformer model.
This has high impact as it clearly demonstrates the various complexities of
quantizing the language translation model. We present novel quantization
techniques directly in TensorFlow to opportunistically replace 32-bit floating
point (FP32) computations with 8-bit integers (INT8) and transform the FP32
computational graph. We also present a bin-packing parallel batching technique
to maximize CPU utilization. Overall, our optimizations with INT8/VNNI deliver
1.5X improvement over the best FP32 performance. Furthermore, it reveals the
opportunities and challenges to boost performance of quantized deep learning
inference and establishes best practices to run inference with high efficiency
on Intel CPUs.
