scholarly journals Towards peptide-based tunable multistate memristive materials

2020 ◽  
Author(s):  
Salvador Cardona-Serra ◽  
Lorena Estefanía Rosaleny ◽  
Silvia M. Giménez-Santamarina ◽  
Luis Martinez-Gil ◽  
Alejandro Gaita-Ariño
Keyword(s):  
Neuromorphic Computing ◽  
Technological Requirement ◽  
Molecular Spintronics

Development of new memristive hardware is a technological requirement towards widespread neuromorphic computing. Molecular spintronics seems a fertile field for the design and preparation of this hardware. Within molecular spintronics,...

Controlling Through-Space and Through-Bond Exchange Pathways in Bis-Cobaltocenes for Molecular Spintronics

2019 ◽  
Author(s):  
Sarah Puhl ◽  
Torben Steenbock ◽  
Carmen Herrmann ◽  
Jürgen Heck
Keyword(s):  
Chemical Synthesis ◽  
First Principles ◽  
Information Transfer ◽  
Spin Coupling ◽  
Molecular Spintronics ◽  
Pathways Analysis ◽  
Chemical Strain

Pinching molecules via chemical strain suggests intuitive consequences, such as compression at the pinched site, and clothespin-like opening of other parts of the structure. If this opening affects two spin centers, it should result in reduced communication between them. We show that for a naphthalene-bridged biscobaltocenes with competing through-space and through-bond pathways, the consequences of pinching are far less intuitive: despite the known dominance of through-space interactions, the bridge plays a much larger role for exchange spin coupling than previously assumed. Based on a combination of chemical synthesis, structural, magnetic and redox characterization, and a newly developed first-principles theoretical pathways analysis, we can suggest a comprehensive explanation for this nonintuitive behavior. These results are of interest for molecular spintronics, as naphthalene-linked cobaltocenes can form wires on surfaces for potential spin-only information transfer.

Foundations of Neuromorphic Computing

2013 ◽  
Author(s):  
Clare Thiem ◽  
Bryant Wysocki ◽  
Morgan Bishop ◽  
Nathan McDonald ◽  
James Bohl
Keyword(s):  
Neuromorphic Computing

Neuromorphic Computing for Very Large Test and Evaluation Data Analysis

2014 ◽  
Author(s):  
Bryant Wysocki ◽  
Nathan McDonald ◽  
Clare Thiem ◽  
Thomas Renz ◽  
James Bohl
Keyword(s):  
Data Analysis ◽  
Neuromorphic Computing ◽  
Evaluation Data ◽  
Test And Evaluation

Embracing the era of neuromorphic computing

2021 ◽  
Vol 42 (1) ◽  
pp. 010301
Author(s):  
Yanghao Wang ◽  
Yuchao Yang ◽  
Yue Hao ◽  
Ru Huang
Keyword(s):  
Neuromorphic Computing

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

scholarly journals Flexible and Transparent Memristive Synapse Based on Polyvinylpyrrolidone/N-doped Carbon Quantum Dot Nanocomposites for Neuromorphic Computing

2021 ◽  
Author(s):  
Tao Zeng ◽  
Zhi Yang ◽  
Jiabing Liang ◽  
Ya Lin ◽  
Yankun Cheng ◽  
...  
Keyword(s):  
Quantum Dot ◽  
Neuromorphic Computing ◽  
Carbon Quantum Dot ◽  
Hardware Implementations

Memristive devices are widely recognized as promising hardware implementations of neuromorphic computing. Herein, a flexible and transparent memristive synapse based on polyvinylpyrrolidone (PVP)/N-doped carbon quantum dot (NCQD) nanocomposites through regulating...

Synaptic devices based neuromorphic computing applications in artificial intelligence

2021 ◽  
pp. 100393
Author(s):  
Bai Sun ◽  
Tao Guo ◽  
Guangdong Zhou ◽  
Shubham Ranjan ◽  
Yixuan Jiao ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neuromorphic Computing

Spin–Orbit Torque‐Induced Domain Nucleation for Neuromorphic Computing

2021 ◽  
pp. 2103672
Author(s):  
Jing Zhou ◽  
Tieyang Zhao ◽  
Xinyu Shu ◽  
Liang Liu ◽  
Weinan Lin ◽  
...  
Keyword(s):  
Spin Orbit ◽  
Neuromorphic Computing

scholarly journals Graphene-based 3D XNOR-VRRAM with ternary precision for neuromorphic computing

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Batyrbek Alimkhanuly ◽  
Joon Sohn ◽  
Ik-Joon Chang ◽  
Seunghyun Lee
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Recognition Accuracy ◽  
Material Selection ◽  
Weighted Sum ◽  
Device Design ◽  
Key Factors ◽  
Neuromorphic Computing ◽  
Device Scaling ◽  
The Impact

AbstractRecent studies on neural network quantization have demonstrated a beneficial compromise between accuracy, computation rate, and architecture size. Implementing a 3D Vertical RRAM (VRRAM) array accompanied by device scaling may further improve such networks’ density and energy consumption. Individual device design, optimized interconnects, and careful material selection are key factors determining the overall computation performance. In this work, the impact of replacing conventional devices with microfabricated, graphene-based VRRAM is investigated for circuit and algorithmic levels. By exploiting a sub-nm thin 2D material, the VRRAM array demonstrates an improved read/write margins and read inaccuracy level for the weighted-sum procedure. Moreover, energy consumption is significantly reduced in array programming operations. Finally, an XNOR logic-inspired architecture designed to integrate 1-bit ternary precision synaptic weights into graphene-based VRRAM is introduced. Simulations on VRRAM with metal and graphene word-planes demonstrate 83.5 and 94.1% recognition accuracy, respectively, denoting the importance of material innovation in neuromorphic computing.

Stimuli‐Responsive Memristive Materials for Artificial Synapses and Neuromorphic Computing

2021 ◽  
pp. 2006469
Author(s):  
Hongyu Bian ◽  
Yi Yiing Goh ◽  
Yuxia Liu ◽  
Haifeng Ling ◽  
Linghai Xie ◽  
...  
Keyword(s):  
Stimuli Responsive ◽  
Neuromorphic Computing
Sign in / Sign up

Export Citation Format

Share Document