  With the continuous development of neural networks for computer vision tasks,
more and more network architectures have achieved outstanding success. As one
of the most advanced neural network architectures, DenseNet shortcuts all
feature maps to solve the model depth problem. Although this network
architecture has excellent accuracy with low parameters, it requires an
excessive inference time. To solve this problem, HarDNet reduces the
connections between the feature maps, making the remaining connections resemble
harmonic waves. However, this compression method may result in a decrease in
the model accuracy and an increase in the parameters and model size. This
network architecture may reduce the memory access time, but its overall
performance can still be improved. Therefore, we propose a new network
architecture, ThreshNet, using a threshold mechanism to further optimize the
connection method. Different numbers of connections for different convolution
layers are discarded to accelerate the inference of the network. The proposed
network has been evaluated with image classification using CIFAR 10 and SVHN
datasets under platforms of NVIDIA RTX 3050 and Raspberry Pi 4. The
experimental results show that, compared with HarDNet68, GhostNet, MobileNetV2,
ShuffleNet, and EfficientNet, the inference time of the proposed ThreshNet79 is
5%, 9%, 10%, 18%, and 20% faster, respectively. The number of parameters of
ThreshNet95 is 55% less than that of HarDNet85. The new model compression and
model acceleration methods can speed up the inference time, enabling network
models to operate on mobile devices.
