AI-Augmented Dynamic Partial Reconfiguration for Adaptive Edge Intelligence in FPGA-Based Embedded Systems

Abstract

The growing deployment of real-time smart applications on the edge- smart surveillance, predictive maintenance, embedded analytics to name but a few- have generated a strong demand in low latency, energy-efficient, and reconfigurable hardware. This paper introduces an AI-enhanced adaptable partial reconfiguration (DPR) scheme of field-programmable gate arrays (FPGAs) which is capable of creating adaptive edge intelligence. The system is able to dynamically rearrange logic regions pre-defined with light weight machine learning approaches to predict workloads in real time to respond dynamically to application needs. This hardware architecture has modular design, reconfiguration controller, an AI-based scheduler, and FPGA reconfigurable areas. This clever DPR mechanism allows hardware accelerators (e.g. object detection, signal filtering, or anomaly tracking) to be swapped on the fly comparatively to the overall system being idle. It achieves up to 45 percent energy savings, 32 percent task latency reduction, and near-zero impact of perceived throughput in heterogeneous workload when experimentally validated on a Xilinx Zynq-based embedded platform. This work indicates the conceptual feasibility of AI-driven reconfiguration as a primary contributor to responsive, context-aware industrial edge systems, including industry 4.0 and edge systems, as well as smart cities/autonomous systems, that allow scalable edge applications. The framework provides the background pertaining towards the further embedded systems in FPGA which would assist real-time adaptability, smart management of hardware resources and autonomic optimization of workloads.