6 papers
Unleashing Low-Bit Inference on Ascend NPUs: A Comprehensive Evaluation of HiFloat Formats
Pengxiang Zhao, Hui-Ling Zhen, Xing Li +10
As LLMs scale, low-bit floating-point formats like MXFP and NVFP4 offer new opportunities for precision and efficiency. In this work, we evaluate HiFloat (HiF8 and HiF4), a family…
ButterflyQuant: Ultra-low-bit LLM Quantization through Learnable Orthogonal Butterfly Transforms
Bingxin Xu, Zhen Dong, Oussama Elachqar +1
Large language models require massive memory footprints, severely limiting deployment on consumer hardware. Quantization reduces memory through lower numerical precision, but extre…
Transformer-Based Tooth Alignment Prediction With Occlusion And Collision Constraints
ZhenXing Dong, JiaZhou Chen, YangHui Xu
The planning of digital orthodontic treatment requires providing tooth alignment, which not only consumes a lot of time and labor to determine manually but also relays clinical exp…
Stochastic Communication Avoidance for Recommendation Systems
Lutfi Eren Erdogan, Vijay Anand Raghava Kanakagiri, Kurt Keutzer +1
One of the major bottlenecks for efficient deployment of neural network based recommendation systems is the memory footprint of their embedding tables. Although many neural network…
DQRM: Deep Quantized Recommendation Models
Yang Zhou, Zhen Dong, Ellick Chan +3
Large-scale recommendation models are currently the dominant workload for many large Internet companies. These recommenders are characterized by massive embedding tables that are s…
Research on Key Technologies of Infrastructure Digitalization based on Multimodal Spatial Data
Zhanyuan Tian, Tianrui Zhu, Zerui Tian +1
Since NASA put forward the concept of the digital twin in 2010, many industries have put forward the dynamic goal of digital development, and the transportation industry is also am…