The rising overheads of data-movement and limitations of general-purpose processing
architectures have led to a huge surge in the interest in “processing-in-memory”(PIM) …

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 …

To understand and improve DRAM performance, reliability, security, and energy efficiency,
prior works study characteristics of commodity DRAM chips. Unfortunately, state-of-the-art …

Time Series Analysis (TSA) is a critical workload to extract valuable information from
collections of sequential data, eg, detecting anomalies in electrocardiograms. Subsequence …

Neuromorphic systems based on memristor arrays have not only addressed the von
Neumann bottleneck issue but have also enabled the development of computing …

In today's digital computing landscape, In-Memory Computing (IMC) has emerged as a
revolutionary approach to tackling critical energy efficiency and latency challenges …

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 …

In the era of big data, the memory wall between the processor and the memory as well as
leakage current have become major bottlenecks of the traditional CMOS-based Von …

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 …

Convolutional neural networks (CNNs) demonstrate excellent performance in various
applications but have high computational complexity. Quantization is applied to reduce the …