Blog(1)

• [CVPR 2023 Series #3] GENIE: Show Me the Data for Quantization

  Since the scale of the state-of-the-art AI models has become deeper, model compression also has been attracting more attention as a method to let models be deployed on edge devices without accessing cloud servers.

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Publications(25)

• LittleBit: Ultra-Low Bit Quantization via Latent Factorization

  AuthorBanseok Lee, Dongkyu Kim, Youngcheon You, Youngmin Kim

  PublishedNeural Information Processing Systems (NeurIPS)

  Date2025-12-02

• BoA: Attention-aware Post-training Quantization without Backpropagation

  AuthorJunhan Kim, Ho-young Kim, Chungman Lee, Joonyoung Kim, Yongkweon Jeon

  PublishedInternational Conference on Machine Learning (ICML)

  Date2025-07-13

• Progressive Mixed-Precision Decoding for Efficient LLM Inference

  AuthorHao (Mark) Chen, Fuwen Tan, Alexandros Kouris, Royson Lee, Stylianos Venieris

  PublishedInternational Conference on Learning Representation (ICLR)

  Date2025-04-25

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News(4)

• [Blog] LittleBit: Ultra Low-Bit Quantization via Latent Factorization

  Large Language Models (LLMs) have transformed technology, but their massive size presents a significant barrier to deployment. The huge memory and computational costs, especially GPU VRAM, make it difficult to run these powerful models on consumer-grade or edge devices.

• [Blog] A Model for Every User and Budget: Label-Free and Personalized Mixed-Precision Quantization

  Research in Automatic Speech Recognition (ASR) continues to show that larger models yield better results. But while state-of-the-art networks continue to grow with billions of parameters, the difficulty of deploying these models on device also increases.

• [Blog] [CVPR 2023 Series #3] GENIE: Show Me the Data for Quantization

  Since the scale of the state-of-the-art AI models has become deeper, model compression also has been attracting more attention as a method to let models be deployed on edge devices without accessing cloud servers.

View More

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