Hardware implementation of memristor-based artificial neural networks
Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL)
techniques, which rely on networks of connected simple computing units operating in …
techniques, which rely on networks of connected simple computing units operating in …
In‐Memory Vector‐Matrix Multiplication in Monolithic Complementary Metal–Oxide–Semiconductor‐Memristor Integrated Circuits: Design Choices, Challenges, and …
The low communication bandwidth between memory and processing units in conventional
von Neumann machines does not support the requirements of emerging applications that …
von Neumann machines does not support the requirements of emerging applications that …
Roadmap on emerging hardware and technology for machine learning
Recent progress in artificial intelligence is largely attributed to the rapid development of
machine learning, especially in the algorithm and neural network models. However, it is the …
machine learning, especially in the algorithm and neural network models. However, it is the …
In-memory learning with analog resistive switching memory: A review and perspective
In this article, we review the existing analog resistive switching memory (RSM) devices and
their hardware technologies for in-memory learning, as well as their challenges and …
their hardware technologies for in-memory learning, as well as their challenges and …
Advances in emerging memory technologies: From data storage to artificial intelligence
G Molas, E Nowak - Applied Sciences, 2021 - mdpi.com
This paper presents an overview of emerging memory technologies. It begins with the
presentation of stand-alone and embedded memory technology evolution, since the …
presentation of stand-alone and embedded memory technology evolution, since the …
HfO2-based resistive switching memory devices for neuromorphic computing
HfO 2-based resistive switching memory (RRAM) combines several outstanding properties,
such as high scalability, fast switching speed, low power, compatibility with complementary …
such as high scalability, fast switching speed, low power, compatibility with complementary …
A monolithic 3-D integration of RRAM array and oxide semiconductor FET for in-memory computing in 3-D neural network
J Wu, F Mo, T Saraya, T Hiramoto… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
We have monolithically integrated resistance random access memory (RRAM) array with
oxide semiconductor channel access transistor in 3-D stack, achieved uniform memory …
oxide semiconductor channel access transistor in 3-D stack, achieved uniform memory …
Challenges and trends of nonvolatile in-memory-computation circuits for AI edge devices
JM Hung, CJ Jhang, PC Wu, YC Chiu… - IEEE Open Journal of …, 2021 - ieeexplore.ieee.org
Nonvolatile memory (NVM)-based computing-in-memory (nvCIM) is a promising candidate
for artificial intelligence (AI) edge devices to overcome the latency and energy consumption …
for artificial intelligence (AI) edge devices to overcome the latency and energy consumption …
Scaling equilibrium propagation to deep convnets by drastically reducing its gradient estimator bias
Equilibrium Propagation is a biologically-inspired algorithm that trains convergent recurrent
neural networks with a local learning rule. This approach constitutes a major lead to allow …
neural networks with a local learning rule. This approach constitutes a major lead to allow …
Digital biologically plausible implementation of binarized neural networks with differential hafnium oxide resistive memory arrays
The brain performs intelligent tasks with extremely low energy consumption. This work takes
its inspiration from two strategies used by the brain to achieve this energy efficiency: the …
its inspiration from two strategies used by the brain to achieve this energy efficiency: the …