Data movement between memory and processors is a major bottleneck in modern
computing systems. The processing-in-memory (PIM) paradigm aims to alleviate this …

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 …

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 …

Reinforcement Learning (RL) trains agents to learn optimal behavior by maximizing reward
signals from experience datasets. However, RL training often faces memory limitations …

Machine Learning (ML) training on large-scale datasets is a very expensive and time-
consuming workload. Processor-centric architectures (eg, CPU, GPU) commonly used for …

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

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 …

The performance of many applications is limited by the available memory bandwidth. One
approach to improve the performance of such memory-bound applications is to move the …

Programming abstractions and optimizing compilers for energy-efficient computing Page 1
Programming abstractions and optimizing compilers for energy-efficient computing Jeronimo …