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(19)

• Z-FOLD: A Frustratingly Easy Post-Training Quantization Scheme for LLMs

  AuthorYongkweon Jeon, Chungman Lee, Kyungphil Park, Ho-young Kim

  PublishedConference on Empirical Methods in Natural Language Processing (EMNLP)

  Date2023-12-06

• Genie: Show Me the Data for Quantization

  AuthorYongkweon Jeon, Chungman Lee, Ho-young Kim

  PublishedComputer Vision and Pattern Recognition(CVPR)

  Date2023-06-18

• NAWQ-SR: A Hybrid-Precision NPU Engine for Efficient On-Device Super-resolution

  AuthorStylianos I. Venieris, Royson Lee

  PublishedIEEE Transactions on Mobile Computing

  Date2023-03-13

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News(3)

• [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.

• [Blog] A Mixed Quantization Network for Efficient Mobile Inverse Tone Mapping

  At the British Machine Vision Conference (BMVC) 2021, we disseminated work on mobile inverse tone mapping. In this work, we tackled converting high resolution images to high dynamic range (HDR) images in real-time with a mobile-focused model which utilized low-bit quantization of its parameters in order to accelerate inference.

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