CA-PPO-AXI: Coverage-Aware Deep Reinforcement Learning for Adaptive AXI Bus Protocol Verification
DOI:
https://doi.org/10.66238/fsrma102Keywords:
AXI bus verification, deep reinforcement learning, coverage-driven verification, Proximal Policy Optimization, adaptive stimulus generationAbstract
The verification of Advanced eXtensible Interface (AXI) bus protocols, such as AXI4 and AXI5, plays a crucial role in ensuring the functional correctness and reliability of modern System-on-Chip (SoC) designs, including CPUs, NPUs, and high-performance server processors. However, the increasing protocol complexity, characterized by burst transactions, multiple outstanding requests, out-of-order responses, and quality-of-service (QoS) mechanisms, makes traditional constraint-random verification inefficient and heavily dependent on manual testbench development. In particular, achieving high functional and assertion coverage for rare corner-case scenarios remains a major challenge. To address these issues, this paper proposes CA-PPO-AXI, a Coverage-Aware Proximal Policy Optimization framework for adaptive AXI bus protocol verification stimulus generation based on deep reinforcement learning. The AXI verification process is formulated as a Markov Decision Process, where a reinforcement learning agent dynamically generates transaction-level stimuli by jointly observing protocol state information and real-time coverage feedback. A coverage-aware state representation and a reward function explicitly designed to encourage coverage improvement and corner-case exploration are introduced to guide policy learning. By leveraging the stability and efficiency of the PPO algorithm, CA-PPO-AXI enables closed-loop, self-adaptive stimulus generation without manual constraint tuning. Experimental results on representative AXI verification scenarios show that CA-PPO-AXI reaches 95.6% functional coverage in 4,700 simulation cycles, reducing coverage convergence time by approximately 52% compared with constraint-random verification. The method also triggers 53 corner-case events, nearly doubling the detection rate of CRV. These results demonstrate that CA-PPO-AXI offers an efficient and scalable approach for intelligent, coverage-aware AXI protocol verification in advanced SoC design flows.
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