Adaptive AIE-PL Systems for Efficient End-to-End Pyramidal 3D Image Registration

Modern accelerators maximize throughput through aggressive specialization. However, in many real-world applications, workloads often vary at runtime, requiring multiple bitstreams to handle such changes. As a result, frequent reconfigurations introduce substantial overhead that can dominate end-to-end execution time. This issue is particularly evident in AIE-PL systems, where statically scheduled AI Engines (AIEs) achieve high performance through compile-time optimization and are therefore typically tailored to fixed workloads. Although AIEs support Runtime Parameters (RTPs) under Processing System (PS) orchestration, RTPs are impractical for discrete hosts. For this reason, we present a structured approach to designing single-bitstream, runtime-adaptable AIE-PL accelerators that does not rely on RTPs, suitable for discrete hosts. We exploit the Programmable Logic (PL) to generate and stream a compact metadata packet that distributes workload configuration across a directed AIE graph before computation. By doing so, we deliberately trade a fraction of fixed-instance efficiency for flexibility. We validate our approach by devising PeterPan, a software-programmable AIE-PL accelerator for 3D image registration. PeterPan supports runtime-varying problem sizes and integrates seamlessly into multi-stage pipelines, such as pyramidal (coarse-to-fine) registration. To maximize PeterPan utilization, we couple it with an ad-hoc software module that employs a novel heuristic to rapidly select informative sub-volumes, keeping the accelerator continuously fed and preventing input-side stalls. On a VCK5000, PeterPan matches state-of-the-art accelerator performance while retaining software programmability. In the end-to-end task, instead, PeterPan delivers a 3.06× speedup and a 2.74× higher energy efficiency than the state-of-the-art AIE-PL accelerator.