  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 (4.79% top-1 error), CIFAR-100
(21.98% top-1 error) and ImageNet (7.91% single model, single crop top-5
error). CondenseNeXt achieves final trained model size improvement of 2.9+ MB
and up to 59.98% reduction in forward FLOPs compared to CondenseNet and can
perform image classification on ARM-Based computing platforms without needing a
CUDA enabled GPU support, with outstanding efficiency.
