Deep learning based on neural networks has been widely used in image recognition,
speech recognition, natural language processing, automatic driving, and other fields and …

Sparse-sparse matrix multiplication (SpGEMM) is a computation kernel widely used in
numerous application domains such as data analytics, graph processing, and scientific …

State-of-the-art deep neural networks (DNNs) have hundreds of millions of connections and
are both computationally and memory intensive, making them difficult to deploy on …

Long Short-Term Memory (LSTM) is widely used in speech recognition. In order to achieve
higher prediction accuracy, machine learning scientists have built increasingly larger …

Neural networks based on Long Short-Term Memory (LSTM) are widely deployed in latency-
sensitive language and speech applications. To speed up LSTM inference, previous …

Tensor factorizations are powerful tools in many machine learning and data analytics
applications. Tensors are often sparse, which makes sparse tensor factorizations memory …

Recurrent Neural Networks (RNNs) are widely used in speech recognition and natural
language processing applications because of their capability to process temporal …

A growing number of commercial and enterprise systems increasingly rely on compute-
intensive Machine Learning (ML) algorithms. While the demand for these compute-intensive …

Graph processing is typically memory bound due to low compute to memory access ratio
and irregular data access pattern. The emerging high-bandwidth memory (HBM) delivers …

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