The prediction of global solar radiation for the regions is of great importance in terms of
giving directions of solar energy conversion systems (design, modeling, and operation) …

Embedded systems technology is undergoing a phase of transformation owing to the novel
advancements in computer architecture and the breakthroughs in machine learning …

As von Neumann computing architectures become increasingly constrained by data-
movement overheads, researchers have started exploring in-memory computing (IMC) …

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 …

The prediction of global solar radiation in a region is of great importance as it provides
investors and politicians with more detailed knowledge about the solar resource of that …

In this paper, we present a 10T SRAM compute-in memory (CiM) macro to process the
multiplication-accumulation (MAC) operations between ternary-inputs and binary-weights. In …

Data reduction in storage systems is an effective solution to minimize the management cost
of a data center. To maximize data-reduction efficiency, prior works propose post …

This paper presents kNN STreaming Unit For Fpgas (kNN-STUFF), a modular, scalable and
efficient Hardware/Software implementation of k-Nearest Neighbors (kNN) classifier …

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 …

Modern Machine Learning (ML) training on large-scale datasets is a very time-consuming
workload. It relies on the optimization algorithm Stochastic Gradient Descent (SGD) due to …