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 …

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 …

Stochastic gradient descent (SGD) is the cornerstone of modern ML systems. Despite its
computational efficiency, SGD requires random data access that is inherently inefficient …

Modern machine learning (ML) systems commonly use stochastic gradient descent (SGD) to
train ML models. However, SGD relies on random data order to converge, which usually …

Decision forest, including RandomForest, XGBoost, and LightGBM, dominates the machine
learning tasks over tabular data. Recently, several frameworks were developed for decision …

The big data representation and processing method based on multimodal tensors can
achieve the fusion representation of different types of data, and perform correlation analysis …