Edge accelerator for lifelong deep learning using streaming linear discriminant analysis

Edge accelerator for lifelong deep learning using streaming linear discriminant analysis

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 accelerat...

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Bibliographic Details
Main Authors: Piyasena, Duvindu, Lam, Siew-Kei, Wu, Meiqing
Other Authors: College of Computing and Data Science
Format: Conference or Workshop Item
Language:English
Published: 2024
Subjects:
Computer and Information Science
FPGA
Deep learning
Online Access:https://hdl.handle.net/10356/178585
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Institution: Nanyang Technological University
Language: English
Description
Summary: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.

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