  One of the main barriers for deploying neural networks on embedded systems
has been large memory and power consumption of existing neural networks. In
this work, we introduce SqueezeNext, a new family of neural network
architectures whose design was guided by considering previous architectures
such as SqueezeNet, as well as by simulation results on a neural network
accelerator. This new network is able to match AlexNet's accuracy on the
ImageNet benchmark with $112\times$ fewer parameters, and one of its deeper
variants is able to achieve VGG-19 accuracy with only 4.4 Million parameters,
($31\times$ smaller than VGG-19). SqueezeNext also achieves better top-5
classification accuracy with $1.3\times$ fewer parameters as compared to
MobileNet, but avoids using depthwise-separable convolutions that are
inefficient on some mobile processor platforms. This wide range of accuracy
gives the user the ability to make speed-accuracy tradeoffs, depending on the
available resources on the target hardware. Using hardware simulation results
for power and inference speed on an embedded system has guided us to design
variations of the baseline model that are $2.59\times$/$8.26\times$ faster and
$2.25\times$/$7.5\times$ more energy efficient as compared to
SqueezeNet/AlexNet without any accuracy degradation.
