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 …

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 …

Many modern workloads such as neural network inference and graph processing are
fundamentally memory-bound. For such workloads, data movement between memory and …

Machine learning (ML) algorithms [1]–[6] have become ubiquitous in many fields of science
and technology due to their ability to learn from and improve with experience with minimal …

As a promising architectural paradigm for applications which demand high I/O bandwidth,
Processing-in-Memory (PIM) computing techniques have been adopted in designing …

Many modern and emerging applications must process increasingly large volumes of data.
Unfortunately, prevalent computing paradigms are not designed to efficiently handle such …

Many modern workloads, such as neural networks, databases, and graph processing, are
fundamentally memory-bound. For such workloads, the data movement between main …

Data movement between main memory and the processor is a significant contributor to the
execution time and energy consumption of memory-intensive applications. This data …

Advances in machine learning (ML) have ignited hardware innovations for efficient
execution of the ML models many of which are memory-bound (eg, long short-term …

Due to the need for computing models that can process large quantities of data efficiently
and with high throughput in many state-of-the-art machine learning algorithms, the …