Prediction queries are widely used across industries to perform advanced analytics and
draw insights from data. They include a data processing part (eg, for joining, filtering …

The huge demand for computation in artificial intelligence (AI) is driving unparalleled
investments in hardware and software systems for AI. This leads to an explosion in the …

Tensor programs often need to process large tensors (vectors, matrices, or higher order
tensors) that require a specialized storage format for their memory layout. Several such …

While loop reordering and fusion can make big impacts on the constant-factor performance
of dense tensor programs, the effects on sparse tensor programs are asymptotic, often …

Deep learning (DL) is growing in popularity for many data analytics applications, including
among enterprises. Large business-critical datasets in such settings typically reside in …

The relational data model was designed to facilitate large-scale data management and
analytics. We consider the problem of how to differentiate computations expressed …

In this paper, we propose nsDB, a novel neuro-symbolic database system that integrates
neural and symbolic system architectures natively to address the weaknesses of each …

Recent years have witnessed a growing list of systems for distributed data-parallel training.
Existing systems largely fit into two paradigms, ie, parameter server and MPI-style collective …

We introduce indexed streams, a formal operational model and intermediate representation
that describes the fused execution of a contraction language that encompasses both sparse …

There are significant benefits to serve deep learning models from relational databases. First,
features extracted from databases do not need to be transferred to any decoupled deep …