  The tremendous potential exhibited by deep learning is often offset by
architectural and computational complexity, making widespread deployment a
challenge for edge scenarios such as mobile and other consumer devices. To
tackle this challenge, we explore the following idea: Can we learn generative
machines to automatically generate deep neural networks with efficient network
architectures? In this study, we introduce the idea of generative synthesis,
which is premised on the intricate interplay between a generator-inquisitor
pair that work in tandem to garner insights and learn to generate highly
efficient deep neural networks that best satisfies operational requirements.
What is most interesting is that, once a generator has been learned through
generative synthesis, it can be used to generate not just one but a large
variety of different, unique highly efficient deep neural networks that satisfy
operational requirements. Experimental results for image classification,
semantic segmentation, and object detection tasks illustrate the efficacy of
generative synthesis in producing generators that automatically generate highly
efficient deep neural networks (which we nickname FermiNets) with higher model
efficiency and lower computational costs (reaching >10x more efficient and
fewer multiply-accumulate operations than several tested state-of-the-art
networks), as well as higher energy efficiency (reaching >4x improvements in
image inferences per joule consumed on a Nvidia Tegra X2 mobile processor). As
such, generative synthesis can be a powerful, generalized approach for
accelerating and improving the building of deep neural networks for on-device
edge scenarios.
