  Over the last five years Deep Neural Nets have offered more accurate
solutions to many problems in speech recognition, and computer vision, and
these solutions have surpassed a threshold of acceptability for many
applications. As a result, Deep Neural Networks have supplanted other
approaches to solving problems in these areas, and enabled many new
applications. While the design of Deep Neural Nets is still something of an art
form, in our work we have found basic principles of design space exploration
used to develop embedded microprocessor architectures to be highly applicable
to the design of Deep Neural Net architectures. In particular, we have used
these design principles to create a novel Deep Neural Net called SqueezeNet
that requires as little as 480KB of storage for its model parameters. We have
further integrated all these experiences to develop something of a playbook for
creating small Deep Neural Nets for embedded systems.
