Interconnect scaling has become a major design challenge for traditional planar (2D)
integrated circuits (ICs). Three-dimensional (3D) IC that stacks multiple device layers …

This comprehensive review explores the advancements in processing-in-memory (PIM)
techniques and chiplet-based architectures for deep neural networks (DNNs). It addresses …

Parameter changes in the complex internal structure of multi-layer 3D stacked chips will
greatly reduce the efficiency of modeling and thermal analysis. In this work, by combining …

The rising use of deep learning and other big-data algorithms has led to an increasing
demand for hardware platforms that are computationally powerful, yet energy-efficient. Due …

In the emerging data-driven science paradigm, computing systems ranging from IoT and
mobile to manycores and datacenters play distinct roles. These systems need to be …

Convolutional neural networks have been proposed as an approach for classifying data
corresponding to a variety of datasets. Indeed, developments in data diversity and …

Heterogeneous architectures are vastly used in various high performance computing
systems from IoT‐based embedded architectures to edge and cloud systems. Although …

The widespread adoption of big data has led to the search for highperformance and low-
power computational platforms. Emerging heterogeneous manycore processing platforms …

Due to the growing needs of Big Data applications (eg, deep learning, graph analytics, and
scientific computing) and the ending of Moore's law, there is a great need for low-cost, high …

Deep neural networks (DNNs) have been employed to different devices as a popular
machine learning algorithm (ML) owing to deploy the Internet of Things (IoT), data mining in …