Sparse matrix–matrix multiplication underpins domains such as scientific computing, graph analytics, and machine learning, but remains a double-edged sword: creating opportunities for acceleration while posing significant challenges for accelerator design. Additionally, as modern sparse workloads grow increasingly heterogeneous, static sparse accelerator designs struggle to sustain high performance across diverse characteristics. Reconfigurable computing offers a promising path by enabling hardware to adapt its dataflows and resource allocation at runtime, but doing so effectively requires principled methods beyond simple heuristics. This talk introduces two complementary approaches that advance both the efficiency and adaptability of sparse accelerators. First, Boötes will be introduced as a spectral-clustering–based technique that reorders sparse matrices to reduce off-chip memory traffic during row-wise multiplication, aligning data access patterns with operand reuse to deliver performance gains across multiple state-of-the-art accelerator architectures while significantly lowering preprocessing costs. Then, Misam will be introduced as a machine-learning–assisted framework that dynamically selects optimal dataflows for sparse matrix multiplication, overcoming the rigidity of fixed designs and achieving substantial speedups with minimal FPGA reconfiguration overhead. Together, these approaches illustrate how combining adaptive machine learning strategies with algorithmic data reordering paves the way for the next generation of versatile and efficient sparse accelerators.