  Pipeline parallelism has achieved great success in deploying large-scale
transformer models in cloud environments, but has received less attention in
edge environments. Unlike in cloud scenarios with high-speed and stable network
interconnects, dynamic bandwidth in edge systems can degrade distributed
pipeline performance. We address this issue with QuantPipe, a
communication-efficient distributed edge system that introduces post-training
quantization (PTQ) to compress the communicated tensors. QuantPipe uses
adaptive PTQ to change bitwidths in response to bandwidth dynamics, maintaining
transformer pipeline performance while incurring limited inference accuracy
loss. We further improve the accuracy with a directed-search analytical
clipping for integer quantization method (DS-ACIQ), which bridges the gap
between estimated and real data distributions. Experimental results show that
QuantPipe adapts to dynamic bandwidth to maintain pipeline performance while
achieving a practical model accuracy using a wide range of quantization
bitwidths, e.g., improving accuracy under 2-bit quantization by 15.85\% on
ImageNet compared to naive quantization.
