In recent years, Deep Neural Networks (DNNs) have achieved unprecedented accuracy across a wide range of tasks. These gains, however, come with substantial increases in model complexity and per‑inference computational cost. As a result, deploying DNNs presents new challenges, and devising efficient methods to execute their computations has become a central concern in research. Modern DNN workloads comprise many nested loops, with Multiply‑and-Accumulate (MAC) operations dominating. In this context, Systolic Arrays (SAs) have emerged as an architecture that connects and coordinates large numbers of Processing Elements (PEs) operating in parallel. Usually, SAs are composed of PEs that communicate only with their direct neighbors.