Large-Scale and Flexible Optical Synapses for Neuromorphic Computing and Integrated Visible Information Sensing Memory Processing

ACS Nano ◽  
2020 ◽  
Author(s):  
Ya-Xin Hou ◽  
Yi Li ◽  
Zhi-Cheng Zhang ◽  
Jia-Qiang Li ◽  
De-Han Qi ◽  
...  
Keyword(s):  
Large Scale ◽  
Neuromorphic Computing ◽  
Memory Processing

Event Memory Uniquely Predicts Memory for Large-Scale Space

2018 ◽  
Author(s):  
Jesse Q. Sargent ◽  
Jeffrey M. Zacks ◽  
David Z. Hambrick ◽  
Nan Lin
Keyword(s):  
Episodic Memory ◽  
Spatial Memory ◽  
Cognitive Abilities ◽  
Large Scale ◽  
Temporal Structure ◽  
Scale Space ◽  
Event Memory ◽  
Memory Processing ◽  
Map Construction ◽  
And Gender

When a person explores a new environment, they begin to construct a spatial representation of it. Doing so is important for navigating and remaining oriented. How does one’s ability to learn a new environment relate to one’s ability to remember experiences in that environment? Here, 208 adults experienced a first-person videotaped route, and then completed a spatial map construction task. They also took tests of general cognitive abilities (working memory, laboratory episodic memory, processing speed, general knowledge) and of memory for familiar, everyday activities (event memory). Regression analyses revealed that event memory (memory for everyday events and their temporal structure), laboratory episodic memory (memory for words and pictures) and gender were unique predictors of spatial memory. These results implicate the processing of temporal structure and organization as an important cognitive ability in large-scale spatial memory from route experience. Accounting for the temporal structure of people’s experience while learning the layout of novel spaces may improve interventions for addressing navigation problems.

Coreset: Hierarchical Neuromorphic Computing Supporting Large-Scale Neural Networks with Improved Resource Efficiency

2021 ◽  
Author(s):  
Liwei Yang ◽  
Huaipeng Zhang ◽  
Tao Luo ◽  
Chuping Qu ◽  
Myat Thu Linn Aung ◽  
...  
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Resource Efficiency ◽  
Neuromorphic Computing

scholarly journals A Memristor-Based Cascaded Neural Networks for Specific Target Recognition

2019 ◽  
Author(s):  
Sheng-Yang Sun ◽  
Hui Xu ◽  
Jiwei Li ◽  
Yi Sun ◽  
Qingjiang Li ◽  
...  
Keyword(s):  
Large Scale ◽  
Recognition Accuracy ◽  
Circuit Simulation ◽  
High Yield ◽  
Small Scale ◽  
Neuromorphic Computing ◽  
Split Method ◽  
High Recognition Accuracy ◽  
Memristor Crossbar ◽  
Computational Ability

Multiply-accumulate calculations using a memristor crossbar array is an important method to realize neuromorphic computing. However, the memristor array fabrication technology is still immature, and it is difficult to fabricate large-scale arrays with high-yield, which restricts the development of memristor-based neuromorphic computing technology. Therefore, cascading small-scale arrays to achieve the neuromorphic computational ability that can be achieved by large-scale arrays, which is of great significance for promoting the application of memristor-based neuromorphic computing. To address this issue, we present a memristor-based cascaded framework with some basic computation units, several neural network processing units can be cascaded by this means to improve the processing capability of the dataset. Besides, we introduce a split method to reduce pressure of input terminal. Compared with VGGNet and GoogLeNet, the proposed cascaded framework can achieve 93.54% Fashion-MNIST accuracy under the 4.15M parameters. Extensive experiments with Ti/AlOx/TaOx/Pt we fabricated are conducted to show that the circuit simulation results can still provide a high recognition accuracy, and the recognition accuracy loss after circuit simulation can be controlled at around 0.26%.

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