The rapid growth of artificial intelligence and the increasing complexity of neural network
models are driving demand for efficient hardware architectures that can address power …

The wide adoption of convolutional neural networks (CNNs) in many applications has given
rise to unrelenting computational demand and memory requirements. Computing-in-Memory …

In-memory computing (IMC) with non-volatile memories (NVMs) has emerged as a
promising approach to address the rapidly growing computational demands of Deep Neural …

To enable efficient computation for convolutional neural networks, in-memory-computing
(IMC) is proposed to perform computation within memory. However, the non-ideality …

While convolutional neural networks (CNNs) are desired for outstanding performance in
many applications, the energy consumption for inference becomes enormous. Computing-in …

The steel industry has received serious attention under the background of carbon
neutralization and carbon peaking. However, the traditional end-to-end energy consumption …

Computing-in-memory (CIM) architecture is a promising convolutional neural network (CNN)
accelerator known for its highly efficient matrix-vector multiplications (MVMs). However, due …

Artificial intelligence (AI) on the edge has emerged as an important research area in the last
decade to deploy different applications in the domains of computer vision and natural …

In-memory computing (IMC) has become the current trend to accelerate the inference of
deep neural networks (DNNs). Nonetheless, IMC suffers from variations that significantly …

The assessment of soil biological activity is pivotal for demonstrating the advantages of
sustainable agricultural practices. However, traditional laboratory-based methods are often …