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.

Approximate computing that works on less precise data leads to significant performance gains and energy-cost reductions for compute kernels. However, without leveraging the full-stack design of computer systems, modern computer architectures undermine the potential of approximate computing.

As the capacity per unit cost dropping, flash-based SSDs become popular in various computing scenarios. However, the restricted program-erase cycles still severely limit cost-effectiveness of flash-based storage solutions. This paper proposes Pensieve, a machine-learning assisted SSD firmware layer that transparently helps reduce the demand for programs and erases. Pensieve efficiently classifies writing data into different compression categories without hints from software systems. Data with the same category may use a shared dictionary to compress the content, allowing Pensieve to further avoid duplications. As Pensieve does not require any modification in the software stack, Pensieve is compatible with existing applications, file systems and operating systems. With modern SSD architectures, implementing a Pensieve-compliant SSD also requires no additional hardware, providing a drop-in upgrade for existing storage systems. The experimental result on our prototype Pensieve SSD shows that Pensieve can reduce the amount of program operations by 19%, while delivering competitive performance.