Spintronics is one of the growing research areas which has the capability to overcome the
issues of static power dissipation and volatility suffered by the complementary metal-oxide …

Processing-in-memory (PIM) is a promising solution to address the" memory wall"
challenges for future computer systems. Prior proposed PIM architectures put additional …

Data movement between the processing units and the memory in traditional von Neumann
architecture is creating the" memory wall" problem. To bridge the gap, two approaches, the …

Processing-in-memory (PIM) provides high bandwidth, massive parallelism, and high
energy efficiency by implementing computations in main memory, therefore eliminating the …

Personalized recommendation systems leverage deep learning models and account for the
majority of data center AI cycles. Their performance is dominated by memory-bound sparse …

Processing-in-memory (PIM) is rapidly rising as a viable solution for the memory wall crisis,
rebounding from its unsuccessful attempts in 1990s due to practicality concerns, which are …

Sparse matrix-vector multiplication (SpMV) is an important primitive across a wide range of
application domains such as scientific computing and graph analytics. Due to its intrinsic …

Transformer-based models are state-of-the-art for many machine learning (ML) tasks.
Executing Transformer usually requires a long execution time due to the large memory …

In this paper we focus on common data reorganization operations such as shuffle,
pack/unpack, swap, transpose, and layout transformations. Although these operations …

In the past decade, monolithic three dimensional integrated circuits (M3D-ICs) advance fast
and demonstrate several important breakthroughs in the fabrication process and circuit level …