hARMS: A Hardware Acceleration Architecture for Real-Time Event-Based Optical Flow
Author:
Affiliation:
1. University of Pittsburgh, Pittsburgh, PA, USA
2. Biomedical Science Tower 3, University of Pittsburgh, Pittsburgh, PA, USA
Funder
NSF Center for Space, High-Performance, and Resilient Computing (SHREC) Industry and Agency Members
IUCRC Program of the National Science Foundation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
https://ieeexplore.ieee.org/ielam/6287639/9668973/9774999-aam.pdf
Reference21 articles.
1. Event-Based Computation of Motion Flow on a Neuromorphic Analog Neural Platform
2. Event-Based Vision: A Survey
3. Point-to-point connectivity between neuromorphic chips using address events
4. A 240 × 180 130 dB 3 µs Latency Global Shutter Spatiotemporal Vision Sensor
5. A QVGA 143 dB Dynamic Range Frame-Free PWM Image Sensor With Lossless Pixel-Level Video Compression and Time-Domain CDS
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1. A Fast and Accurate Optical Flow Camera for Resource-Constrained Edge Applications;2023 9th International Workshop on Advances in Sensors and Interfaces (IWASI);2023-06-08
2. Tangentially Elongated Gaussian Belief Propagation for Event-Based Incremental Optical Flow Estimation;2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2023-06
3. Event‐based high throughput computing: A series of case studies on a massively parallel softcore machine;IET Computers & Digital Techniques;2022-12-19
4. A High-speed Event-based Object Tracking with Heterogeneous Computing Hardware;2022 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS);2022-11-11
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