Energy efficient architecture is essential to improve both the performance and power
consumption of a computer system. However, modern computers suffer from the severe …

Conventional von Neumann architectures cannot successfully meet the demands of
emerging computation and data-intensive applications. These shortcomings can be …

In-situ approaches process data very close to the memory cells, in the row buffer of each
subarray. This minimizes data movement costs and affords parallelism across subarrays …

Today's systems have diverse needs that are difficult to address using one-size-fits-all
commodity DRAM. Unfortunately, although system designers can theoretically adapt …

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

Training machine learning (ML) algorithms is a computationally intensive process, which is
frequently memory-bound due to repeatedly accessing large training datasets. As a result …

In this paper we propose a Highly Flexible InMemory (HieIM) computing platform using STT
MRAM, which can be leveraged to implement Boolean logic functions without sacrificing …

Near-data accelerators (NDAs) that are integrated with the main memory have the potential
for significant power and performance benefits. Fully realizing these benefits requires the …

Today's computing architectures and device technologies are unable to meet the
increasingly stringent demands on energy and performance posed by emerging …

The confluence of the recent advances in technology and the ever-growing demand for
large-scale data analytics created a renewed interest in a decades-old concept, processing …