  The emergence of novel hardware accelerators has powered the tremendous
growth of machine learning in recent years. These accelerators deliver
incomparable performance gains in processing high-volume matrix operators,
particularly matrix multiplication, a core component of neural network training
and inference. In this work, we explored opportunities of accelerating database
systems using NVIDIA's Tensor Core Units (TCUs). We present TCUDB, a
TCU-accelerated query engine processing a set of query operators including
natural joins and group-by aggregates as matrix operators within TCUs. Matrix
multiplication was considered inefficient in the past; however, this strategy
has remained largely unexplored in conventional GPU-based databases, which
primarily rely on vector or scalar processing. We demonstrate the significant
performance gain of TCUDB in a range of real-world applications including
entity matching, graph query processing, and matrix-based data analytics. TCUDB
achieves up to 288x speedup compared to a baseline GPU-based query engine.
