Recent trends in deep learning (DL) imposed hardware accelerators as the most viable
solution for several classes of high-performance computing (HPC) applications such as …

Neuromorphic computing has recently emerged as a potential alternative to the
conventional von Neumann computer paradigm, which is inherently limited due to its …

This work showcases the implementation of 16 Boolean logic functions through a double-
gate (DG) fully depleted (FD) Silicon-on-Insulator (SOI) Metal-Oxide-Semiconductor-Field …

Deploying Deep Learning (DL) models on edge devices presents several challenges due to
the limited set of processing and memory resources, and the bandwidth constraints while …

The reliability of multilayered spin-transfer torque magnetoresistive random access memory
with synthetic antiferro-magnets is critical for applications such as computing-in-memory …

Integrating RGB and event sensors improves object detection accuracy, especially during
the night, due to the high-dynamic range of event camera. However, introducing an event …

Resistive-switching memory (RRAM) devices are an attractive technology for in-memory
computing (IMC) to accelerate data-intensive tasks, such as deep neural network (DNN) …

To overcome the excessive memory capacity of non-volatile CiM (NV-CiM) for multi-modal
AI, this paper proposes Mapping-oriented enhanced-FPN (Feature Pyramid Network) fusion …

As human civilization has entered its 21 st century, the enhancement of computational
power is high in demand and has presented great challenges for researchers in different …

In-memory computing (IMC) has emerged as a promising solution for artificial intelligence
(AI) accelerators thanks to the reduced data movement and improved parallelism in the …