New computing technologies inspired by the brain promise fundamentally different ways to
process information with extreme energy efficiency and the ability to handle the avalanche of …

In this work, we present a compute-in-memory (CIM) macro built around a standard two-port
compiler macro using foundry 8T bit-cell in 7-nm FinFET technology. The proposed design …

The revolution of machine learning poses an unprecedented demand for computation
resources, urging more transistors on a single monolithic chip, which is not sustainable in …

A novel wafer-scale system integration solution, InFO_SoW (System-on-Wafer), has been
successfully developed to integrate known-good chips arrays with power and thermal …

Convolutional neural networks (CNNs) are at the core of many state-of-the-art deep learning
models in computer vision, speech, and text processing. Training and deploying such CNN …

Modern application-specific System-on-Chip designs include a variety of accelerator blocks
that customize microcontrollers with domain-specific instruction sets and optimized …

Mobile and edge devices become common platforms for inferring convolutional neural
networks (CNNs) due to superior privacy and service quality. To reduce the computational …

The optimization for hardware processor and system for performing deep learning
operations such as Convolutional Neural Networks (CNN) in resource limited embedded …

Energy is a primary constraint in processor design, and much of that energy is consumed in
on-chip communication. Communication can be intra-core (eg, from a register file to an ALU) …

In the current study, the author implemented cohort analysis methodology of machine
learning (ML) to assess the rate of consumers' complaint retention behavior toward firms …