Several manufacturers have already started to commercialize near-bank Processing-In-
Memory (PIM) architectures, after decades of research efforts. Near-bank PIM architectures …

Graph neural networks (GNNs) can extract features by learning both the representation of
each objects (ie, graph nodes) and the relationship across different objects (ie, the edges …

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 …

Responding to the" datacenter tax" and" killer microseconds" problems for memory-intensive
datacenter applications, diverse solutions including Smart NIC-based ones have been …

DIMM-based near-memory processing architectures (DIMM-NMP) have received growing
interest from both academia and industry. They have the advantages of large memory …

Processing-in-memory (PIM) has been explored for decades by computer architects, yet it
has never seen the light of day in real-world products due to its high design overheads and …

Personalized recommendation models (RecSys) are one of the most popular machine
learning workload serviced by hyperscalers. A critical challenge of training RecSys is its …

Graph convolutional neural networks (GCNs) have emerged as a key technology in various
application domains where the input data is relational. A unique property of GCNs is that its …

Ongoing climate change calls for fast and accurate weather and climate modeling. However,
when solving large-scale weather prediction simulations, state-of-the-art CPU and GPU …

Deep learning recommendation systems serve personalized content under diverse tail-
latency targets and input-query loads. In order to do so, state-of-the-art recommendation …