  Sparse matrix-matrix multiplication (SpGEMM) is a critical operation in
numerous fields, including scientific computing, graph analytics, and deep
learning. These applications exploit the sparsity of matrices to reduce storage
and computational demands. However, the irregular structure of sparse matrices
poses significant challenges for performance optimization. Traditional hardware
accelerators are tailored for specific sparsity patterns with fixed dataflow
schemes - inner, outer, and row-wise but often perform suboptimally when the
actual sparsity deviates from these predetermined patterns. As the use of
SpGEMM expands across various domains, each with distinct sparsity
characteristics, the demand for hardware accelerators that can efficiently
handle a range of sparsity patterns is increasing. This paper presents a
machine learning based approach for adaptively selecting the most appropriate
dataflow scheme for SpGEMM tasks with diverse sparsity patterns. By employing
decision trees and deep reinforcement learning, we explore the potential of
these techniques to surpass heuristic-based methods in identifying optimal
dataflow schemes. We evaluate our models by comparing their performance with
that of a heuristic, highlighting the strengths and weaknesses of each
approach. Our findings suggest that using machine learning for dynamic dataflow
selection in hardware accelerators can provide upto 28 times gains.
