EyeCoD: Eye Tracking System Acceleration via FlatCam-Based Algorithm and Hardware Co-Design
Author:
Affiliation:
1. Georgia Institute of Technology, Atlanta, GA, USA
2. Rice University, Houston, TX, USA
3. Meta Reality Labs, Redmond, WA, USA
Funder
National Science Foundation’s Real-Time Machine Learning program
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Hardware and Architecture,Software
Link
http://xplorestaging.ieee.org/ielx7/40/10167507/10123119.pdf?arnumber=10123119
Reference12 articles.
1. SACoD: Sensor Algorithm Co-Design Towards Efficient CNN-powered Intelligent PhlatCam
2. i-FlatCam: A 253 FPS, 91.49 µJ/Frame Ultra-Compact Intelligent Lensless Camera for Real-Time and Efficient Eye Tracking in VR/AR
3. e-G2C: A 0.14-to-8.31 µJ/Inference NN-based Processor with Continuous On-chip Adaptation for Anomaly Detection and ECG Conversion from EGM
4. RITnet: Real-time Semantic Segmentation of the Eye for Gaze Tracking
5. OpenEDS2020: Open eyes dataset;palmero,2020
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