The constant drive to achieve higher performance in deep neural networks (DNNs) has led
to the proliferation of very large models. Model training, however, requires intensive …

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 development of artificial intelligence has continuously amplified the demand for
neuromorphic computing and memory. However, the separation of computing and memory …

Processing-in-memory (PIM) architectures have been highlighted as one of the viable
solutions for faster and more power-efficient convolutional neural networks (CNNs) …

Compute-in-Memory (CIM) implemented with Resistive-Random-Access-Memory (RRAM)
crossbars is a promising approach for accelerating Convolutional Neural Network (CNN) …

A static random-access memory (SRAM)-based computing-in-memory (CiM) is a promising
architecture for efficiently performing high-precision integer (INT) multiplication and …

Processing-in-memory (PIM) architectures have been highlighted as one of the most viable
options for faster and more power-efficient computation. Paired with a convolutional weight …

A low-power system with minimal area and high inference accuracy defines the performance
of the neural network accelerators. This tradeoff between accuracy and hardware …

Computing-in-memory (CIM) is a promising technique for hardware acceleration of neural
networks (NNs) with high performance and efficiency. However, conventional dense …

The performance of processor-centric von Neumann architectures is greatly hindered by
data movement between memory and processor, especially when encountering data …