6 papers
Visual Accommodation: Rethinking Image Scale as a Learnable Variable for Object Detection
Daeun Seo, Hoeseok Yang, Sihyeong Park +1
We propose Ciliary-DETR (previous name: Elastic-DETR), a framework for test-time resolution adjustment analogous to biological accommodation. While multi-scale data augmentation im…
A Survey on Inference Engines for Large Language Models: Perspectives on Optimization and Efficiency
Sihyeong Park, Sungryeol Jeon, Chaelyn Lee +3
Large language models (LLMs) are widely applied in chatbots, code generators, and search engines. Workload such as chain-of-throught, complex reasoning, agent services significantl…
Exploring the Trade-Offs: Quantization Methods, Task Difficulty, and Model Size in Large Language Models From Edge to Giant
Jemin Lee, Sihyeong Park, Jinse Kwon +2
Quantization has gained attention as a promising solution for the cost-effective deployment of large and small language models. However, most prior work has been limited to perplex…
Mixed Non-linear Quantization for Vision Transformers
Gihwan Kim, Jemin Lee, Sihyeong Park +2
The majority of quantization methods have been proposed to reduce the model size of Vision Transformers, yet most of them have overlooked the quantization of non-linear operations.…
A Review on Proprietary Accelerators for Large Language Models
Sihyeong Park, Jemin Lee, Byung-Soo Kim +1
With the advancement of Large Language Models (LLMs), the importance of accelerators that efficiently process LLM computations has been increasing. This paper discusses the necessi…
Q-HyViT: Post-Training Quantization of Hybrid Vision Transformers with Bridge Block Reconstruction for IoT Systems
Jemin Lee, Yongin Kwon, Sihyeong Park +3
Recently, vision transformers (ViTs) have superseded convolutional neural networks in numerous applications, including classification, detection, and segmentation. However, the hig…