MARTE-Based Formal Modeling with Reinforcement Learning for Architecture-Agnostic Assembler Design in Configurable Processors

Abstract

This paper presents a configurable assembler framework enhanced with reinforcement learning (RL) and MARTE (Modeling and Analysis of Real-Time and Embedded Systems) constraints to address the challenges of rapidly evolving processor architectures. Our methodology integrates formal hardware/software modeling with self-optimizing configuration strategies, enabling automatic adaptation to instruction set architecture (ISA) modifications while ensuring correctness guarantees. The framework features a Common ISA Description Language (CIDL) interface that reduces code modification efforts by 85% compared to traditional assemblers, as demonstrated through four representative configuration scenarios. By formulating the adaptation process as a constrained Markov Decision Process, our RL-MARTE hybrid approach achieves 58% fewer configuration steps and 26.5% higher code density, i.e., the ratio of useful instruction bytes to total code size, than rule-based methods, while maintaining zero constraint violations. Experimental results on the TOP-32A processor extension demonstrate the solution’s effectiveness in handling complex ISA modifications like MIMD instructions, reducing average configuration time from 45.6 to 9.8 minutes per task. This work bridges the gap between formal verification and practical adaptability in compiler construction, offering a systematic approach for next-generation processor toolchain development.