Computing-in-memory (CIM) has emerged as an energy-efficient hardware solution for
machine learning and AI. While static random access memory (SRAM)-based CIM has been …

Neural network models have been widely used in various fields as the main way to solve
problems in the current artificial intelligence (AI) field. Efficient execution of neural network …

This article presents Scaling-computing-in-memory (CIM), an energy-efficient embedded
dynamic random access memory (eDRAM)-based in-memory-computing (IMC) accelerator …

Digital computing-in-memory (DCIM) has showcased its superiority in terms of throughput
and accuracy as it scales down toward advanced technology nodes [1]–[3]. However, the …

Computing-in-memory (CIM) architectures based on various devices, such as resistive
random access memory, SRAM, DRAM, etc., have demonstrated promising energy …

This study introduces an event-driven smart vision system-on-chip (SoC) using a
reconfigurable and high-linearity magnetoresistive random access memory (MRAM) …

With the advancement of neural networks, particularly the emergence of large language
models, in-memory computing has garnered significant attention as a solution to address …

This article presents a compute-in-memory (CIM) architecture for a large-scale machine
learning (ML) accelerator, which employs embedded dynamic random access memory …

This study introduces optical neural networks (ONNs) designed to accelerate optical
convolution operations using a red, green, and blue (RGB) pixel array integrated into …

This article reports a high-density 3T1C single-finger (SF) embedded dynamic random
access memory (eDRAM) compute-in-memory (CIM) macro. It features several techniques …