4 papers
TurboBoA: Faster and Exact Attention-aware Quantization without Backpropagation
Junhan Kim, Yeo Jeong Park, Seungwoo Son +4
The rapid growth of large language models (LLMs) has heightened the importance of post-training quantization (PTQ) for reducing memory and computation costs. Among PTQ methods, GPT…
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…
Towards Next-Level Post-Training Quantization of Hyper-Scale Transformers
Junhan Kim, Chungman Lee, Eulrang Cho +4
With the increasing complexity of generative AI models, post-training quantization (PTQ) has emerged as a promising solution for deploying hyper-scale models on edge devices such a…
Genie: Show Me the Data for Quantization
Yongkweon Jeon, Chungman Lee, Ho-young Kim
Zero-shot quantization is a promising approach for developing lightweight deep neural networks when data is inaccessible owing to various reasons, including cost and issues related…