Compute-in-memory (CIM) is a new computing paradigm that addresses the memory-wall
problem in hardware accelerator design for deep learning. The input vector and weight …

With the rapid growth of computer science and big data, the traditional von Neumann
architecture suffers the aggravating data communication costs due to the separated structure …

DNN+ NeuroSim is an integrated framework to benchmark compute-in-memory (CIM)
accelerators for deep neural networks, with hierarchical design options from device-level, to …

Today's high performance computing (HPC) systems are limited by the expensive data
movement between processing and memory units. An emerging solution strategy is to …

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

Different from the deep neural network (DNN) inference process, the training process
produces a huge amount of intermediate data to compute the new weights of the network …

The RRAM-based accelerators have become very popular candidates for neural network
acceleration due to they perform matrix-vector multiplication in-memory with high storage …

ReRAM-based accelerators have shown great potential in neural network acceleration via in-
memory analog computing. However, high-precision analog-to-digital converters (ADCs) …

Neuromorphic computing has emerged as a better alternative for developing next-
generation artificial intelligent systems. Resistive Random Access Memory (RRAM) have …

Embedded memory, which heavily relies on the manufacturing process, has been widely
adopted in various industrial applications. As the field of embedded memory continues to …