4 papers
Prune-then-Quantize or Quantize-then-Prune? Understanding the Impact of Compression Order in Joint Model Compression
Minjun Kim, Jaehyeon Choi, Hyunwoo Yang +3
What happens when multiple compression methods are combined-does the order in which they are applied matter? Joint model compression has emerged as a powerful strategy to achieve h…
SynQ: Accurate Zero-shot Quantization by Synthesis-aware Fine-tuning
Minjun Kim, Jongjin Kim, U Kang
How can we accurately quantize a pre-trained model without any data? Quantization algorithms are widely used for deploying neural networks on resource-constrained edge devices. Zer…
LampQ: Towards Accurate Layer-wise Mixed Precision Quantization for Vision Transformers
Minjun Kim, Jaeri Lee, Jongjin Kim +3
How can we accurately quantize a pre-trained Vision Transformer model? Quantization algorithms compress Vision Transformers (ViTs) into low-bit formats, reducing memory and computa…
Accurate Sublayer Pruning for Large Language Models by Exploiting Latency and Tunability Information
Seungcheol Park, Sojin Lee, Jongjin Kim +3
How can we accelerate large language models(LLMs) without sacrificing accuracy? The slow inference speed of LLMs hinders us to benefit from their remarkable performance in diverse…