Scenario Based Run-Time Switching for Adaptive CNN-Based Applications at the Edge

Abstract

Convolutional Neural Networks (CNNs) are biologically inspired computational models that are at the heart of many modern computer vision and natural language processing applications. Some of the CNN-based applications are executed on mobile and embedded devices. Execution of CNNs on such devices places numerous demands on the CNNs, such as high accuracy, high throughput, low memory cost, and low energy consumption. These requirements are very difficult to satisfy at the same time, so CNN execution at the edge typically involves trade-offs (e.g., high CNN throughput is achieved at the cost of decreased CNN accuracy). In existing methodologies, such trade-offs are either chosen once and remain unchanged during a CNN-based application execution, or are adapted to the properties of the CNN input data. However, the application needs can also be significantly affected by the changes in the application environment, such as a change of the battery level in the edge device. Thus, CNN-based applications need a mechanism that allows to dynamically adapt their characteristics to the changes in the application environment at run-Time. Therefore, in this article, we propose a scenario-based run-Time switching (SBRS) methodology, that implements such a mechanism.