Deep neural network model optimizations for resource constrained tactical edge computing platforms

With the advent of neural networks, users at the tactical edge have started experimenting with AI enabled intelligent mission applications. Autonomy stacks have been proposed for the tactical environments for sensing, reasoning and computing the situational awareness to provide the human in the loop actionable intelligence in mission time. Tactical edge computing platforms must employ small-form-factor modules for compute, storage, and networking functions that conform to strict size, weight, and power constraints (SWaP). Many of the neural network models proposed for the tactical AI stack are computationally complex and may not be deployable without modifications. In this paper we discuss deep neural network optimization approaches for resource constrained tactical unmanned ground vehicles.