TL;DR
This paper introduces CondenseNeXt, an ultra-efficient deep CNN architecture optimized for ARM-based embedded platforms, achieving state-of-the-art accuracy with significantly reduced computational requirements on multiple benchmark datasets.
Contribution
The paper presents CondenseNeXt, a novel CNN architecture that combines depthwise separable convolutions and model compression for high efficiency on ARM-based devices.
Findings
Achieves state-of-the-art accuracy on CIFAR-10, CIFAR-100, and ImageNet datasets.
Reduces FLOPs by up to 59.98% compared to CondenseNet.
Operates efficiently on ARM-based platforms without GPU support.
Abstract
In this paper, we demonstrate the implementation of our ultra-efficient deep convolutional neural network architecture: CondenseNeXt on NXP BlueBox, an autonomous driving development platform developed for self-driving vehicles. We show that CondenseNeXt is remarkably efficient in terms of FLOPs, designed for ARM-based embedded computing platforms with limited computational resources and can perform image classification without the need of a CUDA enabled GPU. CondenseNeXt utilizes the state-of-the-art depthwise separable convolution and model compression techniques to achieve a remarkable computational efficiency. Extensive analyses are conducted on CIFAR-10, CIFAR-100 and ImageNet datasets to verify the performance of CondenseNeXt Convolutional Neural Network (CNN) architecture. It achieves state-of-the-art image classification performance on three benchmark datasets including CIFAR-10…
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Taxonomy
MethodsPointwise Convolution · Convolution · Depthwise Convolution · Depthwise Separable Convolution
