The deployment of Deep Learning Recommendation Models (DLRMs) involves the
parallelization of extra-large embedding tables (EMTs) on multiple GPUs. Existing works …

Recommendation models rely on deep learning networks and large embedding tables,
resulting in computationally and memory-intensive processes. These models are typically …

Processing-in-memory (PIM), where the compute is moved closer to the memory or the data,
has been widely explored to accelerate emerging workloads. Recently, different PIM-based …

Personalized recommendation systems have become one of the most important Internet
services nowadays. A critical challenge of training and deploying the recommendation …

Industrial recommendation models typically involve numerous feature fields. The embedding
computation workloads are heterogeneous across these fields, thus requiring varied optimal …

Real-world objects exhibit intricate semantic properties that can be characterized from a
multitude of perspectives, which necessitates the development of a model capable of …

Deep Learning Recommendation Models (DLRMs) are pivotal in various sectors, yet they
are hindered by the high memory demands of embedding tables and the significant …

Large-scale Ads recommendation and auction scoring models at Google scale demand
immense computational resources. While specialized hardware like TPUs have improved …

DLRM is a state-of-the-art recommendation system model that has gained widespread
adoption across various industry applications. The large size of DLRM models, however …

Photovoltaic (PV) power forecasting is important for promoting the integration of renewable
energy sources. However, neural network-based methods, particularly deep learning for PV …