2 papers
cs.LG2024
BoA: Attention-aware Post-training Quantization without Backpropagation
Junhan Kim, Ho-young Kim, Eulrang Cho +3
Post-training quantization (PTQ) is a promising solution for deploying large language models (LLMs) on resource-constrained devices. Early methods developed for small-scale network…
cs.LG2023
RepQ: Generalizing Quantization-Aware Training for Re-Parametrized Architectures
Anastasiia Prutianova, Alexey Zaytsev, Chung-Kuei Lee +2
Existing neural networks are memory-consuming and computationally intensive, making deploying them challenging in resource-constrained environments. However, there are various meth…