Deep Functional Network (DFN): Functional Interpretation of Deep Neural Networks for Intelligent Sensing Systems

Abstract

We introduce Deep Functional Network (DFN) that approximates a black-box Deep Neural Network (DNN) to a functional program consisting of a set of well-known functions and data flows among them. A DFN not only provides a semantic interpretation of a DNN but also enables easy deployment and optimization of the translated program according to the requirements and constraints of the target intelligent sensing system. To interpret a DNN, we propose the DFN framework consisting of two steps: 1) function estimation that estimates the distribution of functions likely to be used in the source DNN and 2) network formation that finds a functional network in the form of a directed acyclic graph (DAG) given the estimated function distribution. Our empirical study conducted with 16 state-of-the-art DNNs demonstrates that the generated DFNs provide semantic understandings of the DNNs along with comparable classification accuracy to the source DNNs. We implement two intelligent sensing systems that use the proposed DFN: 1) a mobile robot that avoids obstacles detected by a camera and 2) a smartphone-based human activity recognizer using IMU sensors, where different sizes of DFNs are generated to complete the task under various resource budgets, i.e., execution time and energy consumption dynamically imposed by run-time scenarios. The experiment result demonstrates that a set of DFNs generated from a single DNN enable both systems to achieve the desired performance under various resource constraints based on semantic understanding of the DNNs.