MLIR (Multi-Level Intermediate Representation) compiler infrastructure has gained widespread popularity in recent years, serving as a foundation for the development of numerous compilers. It provides a comprehensive set of shared resources benefiting multiple domains simultaneously and introduces dialects to accommodate various levels of abstraction within the representation. Due to its fundamental role in compiler construction, it is critical to ensure its correctness. Recently, a grammar-based fuzzing technique (i.e., MLIRSmith) has been proposed for it and achieves notable effectiveness. However, MLIRSmith generates test programs in a random manner, which restricts the exploration of the input space, thereby limiting the overall fuzzing effectiveness. In this work, we propose a novel fuzzing technique, called MLIRod. As complicated or uncommon data/control dependencies among various operations are often helpful to trigger MLIR bugs, it constructs the operation dependency graph for an MLIR program and defines the associated operation dependency coverage to guide the fuzzing process. To drive the fuzzing process towards increasing operation dependency coverage, MLIRod then designs a set of dependency-targeted mutation rules. By applying MLIRod to the latest revisions of the MLIR compiler infrastructure, it detected 63 previously unknown bugs, among which 38/48 bugs have been fixed/confirmed by developers. Also, MLIRod has demonstrated superior effectiveness compared to the state-of-the-art MLIRSmith, achieving a remarkable 121.43% improvement in bug detection.