English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The high demand for computational and storage resources severely impede thedeployment of deep convolutional neural networks (CNNs) in limited-resourcedevices. Recent CNN architectures have proposed reduced complexity versions(e.g. SuffleNet and MobileNet) but at the cost of modest decreases inaccuracy.This paper proposes pSConv, a pre-defined sparse 2D kernel-based convolution,which promises significant improvements in the trade-off between complexity andaccuracy for both CNN training and inference. To explore the potential of thisapproach, we have experimented with two widely accepted datasets, CIFAR-10 andTiny ImageNet, in sparse variants of both the ResNet18 and VGG16 architectures.Our approach shows a parameter count reduction of up to 4.24x with modestdegradation in classification accuracy relative to that of standard CNNs. Ourapproach outperforms a popular variant of ShuffleNet using a variant ofResNet18 with pSConv having 3x3 kernels with only four of nine elements notfixed at zero. In particular, the parameter count is reduced by 1.7x forCIFAR-10 and 2.29x for Tiny ImageNet with an increased accuracy of ~4%.

A Pre-defined Sparse Kernel Based Convolution for Deep CNNs