Automatic parallelization of sequential programs combined with auto-tuning is an alternative
to manual parallelization. With wider research directions and the increased number of …

In today's data-centric world, where data fuels numerous application domains, with machine
learning at the forefront, handling the enormous volume of data efficiently in terms of time …

In-memory computing (IMC) using emerging nonvolatile devices has received considerable
attention due to its great potential for accelerating artificial neural networks and machine …

Machine learning and data analytics applications increasingly suffer from the high latency
and energy consumption of conventional von Neumann architectures. Recently, several in …

This paper explores the challenges and opportunities of integrating non-volatile memories
(NVMs) into embedded systems for machine learning. NVMs offer advantages such as …

In recent years, various computing-in-memory (CIM) processors have been presented,
showing superior performance over traditional architectures. To unleash the potential of …

Building hybrid systems that incorporate various processing-in-memory (PIM) devices and
processing-near-memory (PNM) technologies can offer complementary advantages in both …

Deep learning (DL) model training must address the memory bottleneck to continue scaling.
Processing-in-memory approaches can be a viable solution as they move computations …

The rise of data-intensive applications exposed the limitations of conventional processor-
centric von-Neumann architectures that struggle to meet the off-chip memory bandwidth …

Multiple research vectors represent possible paths to improved energy and performance
metrics at the application-level. There are active efforts with respect to emerging logic …