8 citations · 13 across the 7 of their papers we have counts for
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cs.LG2024★ 1 cited
Mixture of Scales: Memory-Efficient Token-Adaptive Binarization for Large Language Models
Dongwon Jo, Taesu Kim, Yulhwa Kim +1
Binarization, which converts weight parameters to binary values, has emerged as an effective strategy to reduce the size of large language models (LLMs). However, typical binarizat…
cs.LG2024★ 2 cited
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…
cs.CL2024★ 1 cited
SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks
Jiwon Song, Kyungseok Oh, Taesu Kim +3
Large language models (LLMs) have proven to be highly effective across various natural language processing tasks. However, their large number of parameters poses significant challe…