Pipelines of Intelligent Compilers for Hardware Self-Reconfiguration

Abstract

In the era of dynamically adaptive hardware systems, self-reconfiguration has emerged as a critical capability for meeting the performance, energy efficiency, and functional flexibility demands of modern computing applications. This paper presents a novel pipeline architecture for intelligent compilers specifically designed to enable hardware self-reconfiguration in real time. Traditional hardware compilation processes are inherently static, resulting in rigid architectures that lack adaptability to runtime workload variations or environmental conditions. In contrast, intelligent compiler pipelines leverage artificial intelligence and machine learning techniques to generate and deploy reconfiguration strategies dynamically, ensuring optimal system performance and resource utilization. The proposed architecture introduces a multi-stage intelligent compiler pipeline composed of: (i) high-level specification analyzers, (ii) optimization engines driven by reinforcement learning, (iii) configuration bitstream generators, and (iv) secure runtime deployment modules. Each stage is designed to process application behavior, environmental feedback, and system constraints to determine the most effective hardware configurations. The compiler’s intelligence is further augmented by a learning-based feedback loop that continually refines the configuration policies based on operational outcomes. To validate the proposed approach, a set of experiments were conducted on FPGA-based platforms performing real-time signal processing and neural inference tasks. The results demonstrate that systems utilizing the intelligent compiler pipeline achieve up to 45% improvement in performance-per-watt and 30% reduction in configuration latency compared to conventional reconfiguration methods. Moreover, the proposed pipeline significantly reduces human intervention and design time while enhancing system resilience under dynamic conditions.