Q-PACE is a dynamic precision allocation method for quantization-aware training that uses a second-order sensitivity model to predict loss increases from quantization noise. By periodically recalculating per-layer curvature coefficients and reassigning precision, it improves mixed-precision training for large language models up to 4 B parameters, achieving comparable loss with lower memory usage. The approach shows that quantization sensitivity is predictable by depth and layer type, enabling infrequent, inexpensive recalibration.
By Alexandra Volkova, Matin Ansaripour, Erik Schultheis, Christoph H. Lampert, Dan Alistarh
arXiv:2602. 03120v2 Announce Type: replace-cross Abstract: Post-Training Quantization (PTQ) is essential for deploying Large Language Models (LLMs) on memory-constrained devices, yet it renders models static and difficult to fine-tune.
By Yinggan Xu, Kajetan Schweighofer, Risto Miikkulainen, Xin Qiu
arXiv:2602. 10431v4 Announce Type: replace Abstract: Large language models (LLMs) demand substantial computational and memory resources, posing challenges for efficient deployment.
By Kanghyun Noh, Jinheon Choi, Yulhwa Kim
arXiv:2606. 09734v1 Announce Type: cross Abstract: Training parameterised quantum circuits (PQCs) on quantum hardware is bottlenecked by the measurement cost of gradient estimation, which under the parameter-shift rule scales linearly in the number of trainable parameters and dominates the total shot budget of training at scale.
By Brian Coyle, Snehal Raj, Virag Umathe, El Amine Cherrat, Elham Kashefi
arXiv:2608. 06916v1 Announce Type: new Abstract: Quantized Neural Networks~(QNN) with low-bitwidth data have proven promising in efficient storage and computation on edge devices.
By Zijun Jiang, Yangdi Lyu
arXiv:2605. 25054v2 Announce Type: replace-cross Abstract: Deploying deep neural networks on resource-constrained 6G edge devices demands aggressive compression with minimal accuracy loss.
By Ayush K. Varshney, Konstantinos Vandikas, \v{S}ar\=unas Girdzijauskas, Adam Orucu, Aneta Vulgarakis Feljan