This article presents Computational SRAM (C-SRAM) solution combining In-and Near-
Memory Computing approaches. It allows performing arithmetic, logic, and complex memory …

Machine learning (ML) algorithms [1]–[6] have become ubiquitous in many fields of science
and technology due to their ability to learn from and improve with experience with minimal …

In this paper, we propose a Spin-Orbit Torque Magnetic Random-Access Memory (SOT-
MRAM) array design that can simultaneously work as non-volatile memory and implement a …

Performance improvements from DRAM technology scaling have been lagging behind the
improvements from logic technology scaling for many years. As application demand for main …

Learning from the data stored in a database is an important function increasingly available
in relational engines. Methods using lower precision input data are of special interest given …

The high volumes of data stored in the cloud, coupled with growing concerns about security
and privacy, have motivated research on homomorphic encryption (HE), ie, a technique that …

DNA short read alignment task has become a major sequential bottleneck to humongous
amounts of data generated by next-generation sequencing platforms. In this paper, an …

Generative Adversarial Network (GAN) has emerged as one of the most promising semi-
supervised learning methods where two neural nets train themselves in a competitive …

Partitioning applications between near-data processing (NDP) and host CPU cores causes
inter-segment data movement overhead, which is caused by moving data generated by one …

The separation of memory and computing units in the von Neumann architecture leads to
undesirable energy consumption due to data movement and insufficient memory bandwidth …