2 citations · 2 across the 2 of their papers we have counts for
4 papers
Locality-Aware Redundancy Pruning for LLM Depth Compression
Vincent-Daniel Yun, Youngrae Kim, Woosang Lim +3
Large language models are known to contain representational redundancy across network depth, making depth pruning an effective approach for improving inference efficiency. Existing…
Neural Weight Compression for Language Models
Jegwang Ryu, Minkyu Kim, Seungjun Shin +3
Efficient compression of language model weights is increasingly critical as model scale and deployment grow. Yet, most existing methods rely on handcrafted transforms and heuristic…
Neural Image Compression with Text-guided Encoding for both Pixel-level and Perceptual Fidelity
Hagyeong Lee, Minkyu Kim, Jun-Hyuk Kim +3
Recent advances in text-guided image compression have shown great potential to enhance the perceptual quality of reconstructed images. These methods, however, tend to have signific…
QUICK: Quantization-aware Interleaving and Conflict-free Kernel for efficient LLM inference
Taesu Kim, Jongho Lee, Daehyun Ahn +4
We introduce QUICK, a group of novel optimized CUDA kernels for the efficient inference of quantized Large Language Models (LLMs). QUICK addresses the shared memory bank-conflict p…