  Sparse matrix-matrix multiplication (SpGEMM) is a critical kernel widely
employed in machine learning and graph algorithms. However, real-world
matrices' high sparsity makes SpGEMM memory-intensive. In-situ computing offers
the potential to accelerate memory-intensive applications through high
bandwidth and parallelism. Nevertheless, the irregular distribution of
non-zeros renders SpGEMM a typical unstructured software. In contrast, in-situ
computing platforms follow a fixed calculation manner, making them structured
hardware. The mismatch between unstructured software and structured hardware
leads to sub-optimal performance of current solutions.
  In this paper, we propose SPLIM, a novel in-situ computing SpGEMM
accelerator. SPLIM involves two innovations. First, we present a novel
computation paradigm that converts SpGEMM into structured in-situ
multiplication and unstructured accumulation. Second, we develop a unique
coordinates alignment method utilizing in-situ search operations, effectively
transforming unstructured accumulation into high parallel searching operations.
Our experimental results demonstrate that SPLIM achieves 275.74$\times$
performance improvement and 687.19$\times$ energy saving compared to NVIDIA RTX
A6000 GPU.
