65 citations · 218 across the 26 of their papers we have counts for
6 papers · 1 filter
i-FlatCam: A 253 FPS, 91.49 J/Frame Ultra-Compact Intelligent Lensless Camera for Real-Time and Efficient Eye Tracking in VR/AR
Yang Zhao, Ziyun Li, Yonggan Fu +9
We present a first-of-its-kind ultra-compact intelligent camera system, dubbed i-FlatCam, including a lensless camera with a computational (Comp.) chip. It highlights (1) a predict…
EyeCoD: Eye Tracking System Acceleration via FlatCam-based Algorithm & Accelerator Co-Design
Haoran You, Cheng Wan, Yang Zhao +11
Eye tracking has become an essential human-machine interaction modality for providing immersive experience in numerous virtual and augmented reality (VR/AR) applications desiring h…
DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks
Yonggan Fu, Haichuan Yang, Jiayi Yuan +5
Efficient deep neural network (DNN) models equipped with compact operators (e.g., depthwise convolutions) have shown great potential in reducing DNNs' theoretical complexity (e.g.,…
ShiftAddNAS: Hardware-Inspired Search for More Accurate and Efficient Neural Networks
Haoran You, Baopu Li, Huihong Shi +2
Neural networks (NNs) with intensive multiplications (e.g., convolutions and transformers) are capable yet power hungry, impeding their more extensive deployment into resource-cons…
LDP: Learnable Dynamic Precision for Efficient Deep Neural Network Training and Inference
Zhongzhi Yu, Yonggan Fu, Shang Wu +3
Low precision deep neural network (DNN) training is one of the most effective techniques for boosting DNNs' training efficiency, as it trims down the training cost from the finest…
Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?
Yonggan Fu, Shunyao Zhang, Shang Wu +2
Vision transformers (ViTs) have recently set off a new wave in neural architecture design thanks to their record-breaking performance in various vision tasks. In parallel, to fulfi…