3 papers
cs.LG2025
STaMP: Sequence Transformation and Mixed Precision for Low-Precision Activation Quantization
Marco Federici, Riccardo Del Chiaro, Boris van Breugel +2
Quantization is the key method for reducing inference latency, power and memory footprint of generative AI models. However, accuracy often degrades sharply when activations are qua…
cs.CV2025
HadaNorm: Diffusion Transformer Quantization through Mean-Centered Transformations
Marco Federici, Riccardo Del Chiaro, Boris van Breugel +2
Diffusion models represent the cutting edge in image generation, but their high memory and computational demands hinder deployment on resource-constrained devices. Post-Training Qu…
cs.LG2024
Low-Rank Quantization-Aware Training for LLMs
Yelysei Bondarenko, Riccardo Del Chiaro, Markus Nagel
Large language models (LLMs) are omnipresent, however their practical deployment is challenging due to their ever increasing computational and memory demands. Quantization is one o…