Memory devices and applications for in-memory computing
Traditional von Neumann computing systems involve separate processing and memory
units. However, data movement is costly in terms of time and energy and this problem is …
units. However, data movement is costly in terms of time and energy and this problem is …
Recent progress in analog memory-based accelerators for deep learning
We survey recent progress in the use of analog memory devices to build neuromorphic
hardware accelerators for deep learning applications. After an overview of deep learning …
hardware accelerators for deep learning applications. After an overview of deep learning …
Equivalent-accuracy accelerated neural-network training using analogue memory
Neural-network training can be slow and energy intensive, owing to the need to transfer the
weight data for the network between conventional digital memory chips and processor chips …
weight data for the network between conventional digital memory chips and processor chips …
Three-dimensional memristor circuits as complex neural networks
Constructing a computing circuit in three dimensions (3D) is a necessary step to enable the
massive connections and efficient communications required in complex neural networks. 3D …
massive connections and efficient communications required in complex neural networks. 3D …
[HTML][HTML] Analog architectures for neural network acceleration based on non-volatile memory
Analog hardware accelerators, which perform computation within a dense memory array,
have the potential to overcome the major bottlenecks faced by digital hardware for data …
have the potential to overcome the major bottlenecks faced by digital hardware for data …
Metaplastic and energy-efficient biocompatible graphene artificial synaptic transistors for enhanced accuracy neuromorphic computing
CMOS-based computing systems that employ the von Neumann architecture are relatively
limited when it comes to parallel data storage and processing. In contrast, the human brain …
limited when it comes to parallel data storage and processing. In contrast, the human brain …
Multiscale co-design analysis of energy, latency, area, and accuracy of a ReRAM analog neural training accelerator
Neural networks are an increasingly attractive algorithm for natural language processing
and pattern recognition. Deep networks with> 50 M parameters are made possible by …
and pattern recognition. Deep networks with> 50 M parameters are made possible by …
Room-temperature fabricated multilevel nonvolatile lead-free cesium halide memristors for reconfigurable in-memory computing
TK Su, WK Cheng, CY Chen, WC Wang, YT Chuang… - ACS …, 2022 - ACS Publications
Recently, conductive-bridging memristors based on metal halides, such as halide
perovskites, have been demonstrated as promising components for brain-inspired hardware …
perovskites, have been demonstrated as promising components for brain-inspired hardware …
Mixed-precision deep learning based on computational memory
Deep neural networks (DNNs) have revolutionized the field of artificial intelligence and have
achieved unprecedented success in cognitive tasks such as image and speech recognition …
achieved unprecedented success in cognitive tasks such as image and speech recognition …
Shape‐dependent multi‐weight magnetic artificial synapses for neuromorphic computing
In neuromorphic computing, artificial synapses provide a multi‐weight (MW) conductance
state that is set based on inputs from neurons, analogous to the brain. Herein, artificial …
state that is set based on inputs from neurons, analogous to the brain. Herein, artificial …