3 citations · 10 across the 8 of their papers we have counts for
8 papers
P4Q: Learning to Prompt for Quantization in Visual-language Models
Huixin Sun, Runqi Wang, Yanjing Li +4
Large-scale pre-trained Vision-Language Models (VLMs) have gained prominence in various visual and multimodal tasks, yet the deployment of VLMs on downstream application platforms…
Drop-Connect as a Fault-Tolerance Approach for RRAM-based Deep Neural Network Accelerators
Mingyuan Xiang, Xuhan Xie, Pedro Savarese +3
Resistive random-access memory (RRAM) is widely recognized as a promising emerging hardware platform for deep neural networks (DNNs). Yet, due to manufacturing limitations, current…
YFlows: Systematic Dataflow Exploration and Code Generation for Efficient Neural Network Inference using SIMD Architectures on CPUs
Cyrus Zhou, Zack Hassman, Ruize Xu +3
We address the challenges associated with deploying neural networks on CPUs, with a particular focus on minimizing inference time while maintaining accuracy. Our novel approach is…
Representation Disparity-aware Distillation for 3D Object Detection
Yanjing Li, Sheng Xu, Mingbao Lin +3
In this paper, we focus on developing knowledge distillation (KD) for compact 3D detectors. We observe that off-the-shelf KD methods manifest their efficacy only when the teacher m…
Bi-ViT: Pushing the Limit of Vision Transformer Quantization
Yanjing Li, Sheng Xu, Mingbao Lin +4
Vision transformers (ViTs) quantization offers a promising prospect to facilitate deploying large pre-trained networks on resource-limited devices. Fully-binarized ViTs (Bi-ViT) th…
Q-DETR: An Efficient Low-Bit Quantized Detection Transformer
Sheng Xu, Yanjing Li, Mingbao Lin +4
The recent detection transformer (DETR) has advanced object detection, but its application on resource-constrained devices requires massive computation and memory resources. Quanti…