Recent advances and future prospects for memristive materials, devices, and systems
Memristive technology has been rapidly emerging as a potential alternative to traditional
CMOS technology, which is facing fundamental limitations in its development. Since oxide …
CMOS technology, which is facing fundamental limitations in its development. Since oxide …
Memristor‐Based Neuromorphic Chips
X Duan, Z Cao, K Gao, W Yan, S Sun… - Advanced …, 2024 - Wiley Online Library
In the era of information, characterized by an exponential growth in data volume and an
escalating level of data abstraction, there has been a substantial focus on brain‐like chips …
escalating level of data abstraction, there has been a substantial focus on brain‐like chips …
Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators
Analog in-memory computing—a promising approach for energy-efficient acceleration of
deep learning workloads—computes matrix-vector multiplications but only approximately …
deep learning workloads—computes matrix-vector multiplications but only approximately …
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 …
Mosaic: in-memory computing and routing for small-world spike-based neuromorphic systems
The brain's connectivity is locally dense and globally sparse, forming a small-world graph—
a principle prevalent in the evolution of various species, suggesting a universal solution for …
a principle prevalent in the evolution of various species, suggesting a universal solution for …
Generative complex networks within a dynamic memristor with intrinsic variability
Y Guo, W Duan, X Liu, X Wang, L Wang… - Nature …, 2023 - nature.com
Artificial neural networks (ANNs) have gained considerable momentum in the past decade.
Although at first the main task of the ANN paradigm was to tune the connection weights in …
Although at first the main task of the ANN paradigm was to tune the connection weights in …
A survey on deep learning hardware accelerators for heterogeneous hpc platforms
Recent trends in deep learning (DL) imposed hardware accelerators as the most viable
solution for several classes of high-performance computing (HPC) applications such as …
solution for several classes of high-performance computing (HPC) applications such as …
Implementation of convolutional neural networks in memristor crossbar arrays with binary activation and weight quantization
We propose a hardware-friendly architecture of a convolutional neural network using a 32×
32 memristor crossbar array having an overshoot suppression layer. The gradual switching …
32 memristor crossbar array having an overshoot suppression layer. The gradual switching …
Photonic neural networks and optics-informed deep learning fundamentals
The recent explosive compute growth, mainly fueled by the boost of artificial intelligence (AI)
and deep neural networks (DNNs), is currently instigating the demand for a novel computing …
and deep neural networks (DNNs), is currently instigating the demand for a novel computing …
Unravelling the operation of organic artificial neurons for neuromorphic bioelectronics
P Belleri, J Pons i Tarrés, I McCulloch… - Nature …, 2024 - nature.com
Organic artificial neurons operating in liquid environments are crucial components in
neuromorphic bioelectronics. However, the current understanding of these neurons is …
neuromorphic bioelectronics. However, the current understanding of these neurons is …