3 papers
cs.LG2026
ReQAT: Achieving Full-Precision Reasoning Accuracy with 4-bit Floating-Point Quantization-Aware Training
Janghwan Lee, Sihwa Lee, Jinseok Kim +4
Large Reasoning Models (LRMs) achieve strong problem-solving through long chain-of-thought, but their deployment is constrained by the high cost of full-precision inference and gro…
cs.CV2022
Automatic Network Adaptation for Ultra-Low Uniform-Precision Quantization
Seongmin Park, Beomseok Kwon, Jieun Lim +3
Uniform-precision neural network quantization has gained popularity since it simplifies densely packed arithmetic unit for high computing capability. However, it ignores heterogene…
cs.LG2022
Learning from distinctive candidates to optimize reduced-precision convolution program on tensor cores
Junkyeong Choi, Hyucksung Kwon, Woongkyu Lee +2
Convolution is one of the fundamental operations of deep neural networks with demanding matrix computation. In a graphic processing unit (GPU), Tensor Core is a specialized matrix…