Edge Accelerator for Lifelong Deep Learning using Streaming Linear Discriminant Analysis
- Resource Type
- Conference
- Authors
- Piyasena, Duvindu; Lam, Siew-Kei; Wu, Meiqing
- Source
- 2021 IEEE 29th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM) FCCM Field-Programmable Custom Computing Machines (FCCM), 2021 IEEE 29th Annual International Symposium on. :259-259 May, 2021
- Subject
- Components, Circuits, Devices and Systems
Computing and Processing
Deep learning
Adaptation models
Computational modeling
Real-time systems
Linear discriminant analysis
Convolutional neural networks
Task analysis
FPGA
ASIC
Deep Learning
Continual Learning
Lifelong Learning
Incremental learning
Object Recognition
Object Classification
Computer Vision
online learning
streaming learning
- Language
- ISSN
- 2576-2621
Lifelong deep learning models are expected to continuously adapt and acquire new knowledge in dynamic environments. This capability is essential for numerous vision tasks in robotics and drones, and the models must be deployed on the edge to achieve real-time performance. We propose a FPGA accelerator of a streaming classifier for lifelong deep learning, which is based on streaming linear discriminant analysis (SLDA). When combined with a frozen Convolutional Neural Network (CNN) model, the proposed system is capable of class incremental lifelong learning for object classification.