  Large Language Models (LLMs) are proficient in natural language processing
tasks, but their deployment is often restricted by extensive parameter sizes
and computational demands. This paper focuses on post-training quantization
(PTQ) in LLMs, specifically 4-bit weight and 8-bit activation (W4A8)
quantization, to enhance computational efficiency -- a topic less explored
compared to weight-only quantization. We present two innovative techniques:
activation-quantization-aware scaling (AQAS) and sequence-length-aware
calibration (SLAC) to enhance PTQ by considering the combined effects on
weights and activations and aligning calibration sequence lengths to target
tasks. Moreover, we introduce dINT, a hybrid data format combining integer and
denormal representations, to address the underflow issue in W4A8 quantization,
where small values are rounded to zero. Through rigorous evaluations of LLMs,
including OPT and LLaMA, we demonstrate that our techniques significantly boost
task accuracies to levels comparable with full-precision models. By developing
arithmetic units compatible with dINT, we further confirm that our methods
yield a 2$\times$ hardware efficiency improvement compared to 8-bit integer MAC
unit.
