  The high demand for computational and storage resources severely impede the
deployment of deep convolutional neural networks (CNNs) in limited-resource
devices. 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 and
accuracy for both CNN training and inference. To explore the potential of this
approach, we have experimented with two widely accepted datasets, CIFAR-10 and
Tiny ImageNet, in sparse variants of both the ResNet18 and VGG16 architectures.
Our approach shows a parameter count reduction of up to 4.24x with modest
degradation in classification accuracy relative to that of standard CNNs. Our
approach outperforms a popular variant of ShuffleNet using a variant of
ResNet18 with pSConv having 3x3 kernels with only four of nine elements not
fixed at zero. In particular, the parameter count is reduced by 1.7x for
CIFAR-10 and 2.29x for Tiny ImageNet with an increased accuracy of ~4%.
