With the rise of artificial intelligence in recent years, Deep Neural Networks (DNNs) have
been widely used in many domains. To achieve high performance and energy efficiency …

This work is a survey of neuromorphic computing-in-memory. Unlike existing surveys that
focus on hardware or application-level perspectives, the authors elaborate on architectures …

Deep neural network (DNN) accelerators as an example of domain-specific architecture
have demonstrated great success in DNN inference. However, the architecture acceleration …

This paper first presents an input-stationary (IS) implemented crossbar accelerator (INCA),
supporting inference and training for deep neural networks (DNNs). Processing-in-memory …

Recently, ReRAM crossbar-based deep neural network (DNN) accelerator has been widely
investigated. However, most prior works focus on single-task inference due to the high …

Memristive crossbars based on Non-volatile Memory (NVM) technologies such as RRAM,
have recently shown great promise for accelerating Deep Neural Networks (DNNs). They …

Resistive random access memory (ReRAM) has been proven capable to efficiently perform
in-situ matrix-vector computations in convolutional neural network (CNN) processing. The …

Abstract Machine learning (ML) algorithms try to learn the map** from an input to output
from data rather than through explicit programming. ML uses algorithms that iteratively learn …

Modern Artificial Intelligence (AI) applications are increasingly utilizing multi-tenant deep
neural networks (DNNs), which lead to a significant rise in computing complexity and the …

Processing-in-memory engines have successfully been applied to accelerate deep neural
networks. For improving computing efficiency, spiking-based designs are widely explored …