PREDICTIVE MODEL-BASED SELECTION OF DEEP NEURAL NETWORKS FOR EMBEDDED IMAGE CLASSIFICATION

Dr. Manukonda Divya; Bussa Uday Kiran

PREDICTIVE MODEL-BASED SELECTION OF DEEP NEURAL NETWORKS FOR EMBEDDED IMAGE CLASSIFICATION

Authors: Dr. Manukonda Divya, Bussa Uday Kiran

Unique Paper ID: 167147
Volume: 11
Issue: 3
PageNo: 400-406

Keywords: Deep learning Embedded Predictive model Inference Accuracy

Abstract:
Efficient and accurate picture categorization in the realm of embedded systems is being pursued via the investigation of deep neural networks (DNNs) due to high demand. Nevertheless, the task of choosing the most suitable DNN structure for certain embedded applications continues to be difficult because of limitations on processing resources, power use, and memory.This research introduces an adaptive algorithm to ascertain the appropriate Deep Neural Network (DNN) model for a given input, taking into account the desired level of accuracy and inference time. Our methodology employs machine learning to create a prediction model that efficiently chooses a pre-trained deep neural network (DNN) based on a given input and optimisation constraint. We implement our methodology to the job of image classification and assess its performance on a Jetson TX2 embedded deep learning platform, using the ImageNet ILSVRC 2012 validation dataset for evaluation. We evaluate a variety of prominent deep neural network (DNN) models.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 3
PageNo: 400-406

PREDICTIVE MODEL-BASED SELECTION OF DEEP NEURAL NETWORKS FOR EMBEDDED IMAGE CLASSIFICATION

Available: https://ijirt.org/Article?manuscript=164982

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Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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