The growing demand for domain-specific accelerators in fields such as machine learning, graph analytics, and scientific computing has highlighted the need for productive and efficient hardware design methodologies. High-level synthesis (HLS) offers an attractive solution by generating hardware from high-level code, with loop pipelining as a cornerstone for maximizing throughput in regular computations. However, existing static and dynamic HLS approaches fail to achieve high pipeline utilization for irregular loop nests characterized by data-dependent bounds, unpredictable memory access patterns, and loop-carried dependencies.

To address the pipeline underutilization in irregular loop, this work proposes a new barrier-free pipeline architecture with a cross-level scheduling strategy and the corresponding HLS compiler Selene, that automatically synthesizes the proposed architecture. Our approach introduces a fine-grained pipeline controller and an outer-loop iteration interleaving mechanism, enabling concurrent execution of multiple outer-loop iterations and efficient handling of data dependencies. Implemented within a commercial HLS flow, Vitis HLS, Selene delivers significant speedups of 4.74$\times$ and 5.46$\times$ over both standard static and dynamic HLS tools on a range of irregular workload benchmarks, demonstrating its effectiveness in overcoming challenges to efficient hardware generation for data-dependent applications.