DICE: Dynamic In-Situ Control for Edge-Based Applications

This paper focuses on addressing computational constraints and energy limitations prevalent in edge-based applications through an innovative approach, dynamic in-situ control for edge-based applications (DICE). DICE capitalizes on the burgeoning trend in vehicle sensor technologies, such as camera, Radar, and LiDAR, which are becoming increasingly powerful and capable of performing pre-processing computations. DICE introduces a concept of “downstream offloading”, which distinguishes it from traditional offloading approaches that typically offload computational tasks from edge devices to more powerful Edge Servers. In contrast, DICE offloads part of the computational tasks from the Edge Server to the sensor itself, thereby optimizing data processing at the source and reducing the volume of data transmission required. This approach not only addresses the latency bottleneck frequently encountered in energy-intensive neural networks but also enhances the efficiency of data processing by selectively filtering out non-critical frames based on event-triggering mechanisms. DICE leverages the unique strengths of portable devices such as smartwatches and smartphones, even with their inherent computational and power limitations. The framework consists of an adaptive control layer for dynamic task allocation and an application layer designed to deploy quantized models on System on Chips (SoCs) like TinyML, thereby improving the efficiency of AI-driven applications while conservatively utilizing energy. This system proposes a sustainable, energy-efficient pathway for future edge-based applications.