A Content-Addressable Memory structure using quantum cells in nanotechnology with energy dissipation analysis

2018 ◽  
Vol 537 ◽  
pp. 202-206 ◽  
Author(s):  
Ali Sadoghifar ◽  
Saeed Rasouli Heikalabad
Keyword(s):  
Energy Dissipation ◽  
Memory Structure ◽  
Content Addressable Memory ◽  
Quantum Cells

Midpoint Memory: A Special Memory Structure for Data-Oriented Models Implementation

2015 ◽  
Vol 24 (05) ◽  
pp. 1550063 ◽  
Author(s):  
Saeed Rasouli Heikalabad ◽  
Ahmad Habibizad Navin ◽  
Mehdi Hosseinzadeh ◽  
Telli Oladghaffari
Keyword(s):  
Data Structure ◽  
Data Structures ◽  
Applied Science ◽  
Memory Structure ◽  
Problem Solution ◽  
Memory Module ◽  
Content Addressable Memory ◽  
Fast Access

Introducing data-oriented theory in recent years and its successful applications in engineering and applied science and by requirements to new memory structure for managing data efficiently, lead us to present a new memory module for implementation of data-oriented models. Most of concepts in data-oriented theory are modeled as problem-solution data structure. In this paper for suitable management these kinds of data structures, a special content addressable memory with fast access is designed which is named Midpoint memory.

A Hybrid Neural Network of Addressable and Content-Addressable Memory

2003 ◽  
Vol 13 (03) ◽  
pp. 205-213 ◽  
Author(s):  
Jinwen Ma
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Theoretical Analysis ◽  
Learning And Memory ◽  
Competitive Learning ◽  
Memory Structure ◽  
Hybrid Neural Network ◽  
Content Addressable Memory ◽  
Learning Rules ◽  
The Brain

We investigate the memory structure and retrieval of the brain and propose a hybrid neural network of addressable and content-addressable memory which is a special database model and can memorize and retrieve any piece of information (a binary pattern) both addressably and content-addressably. The architecture of this hybrid neural network is hierarchical and takes the form of a tree of slabs which consist of binary neurons with the same array. Simplex memory neural networks are considered as the slabs of basic memory units, being distributed on the terminal vertexes of the tree. It is shown by theoretical analysis that the hybrid neural network is able to be constructed with Hebbian and competitive learning rules, and some other important characteristics of its learning and memory behavior are also consistent with those of the brain. Moreover, we demonstrate the hybrid neural network on a set of ten binary numeral patters.

Energy Dissipation Processes of Singlet Excited 1-Hydroxyfluorenone and its Hydrogen-Bonded Complex with N-Methylimidazole

2004 ◽  
Author(s):  
Krisztina Sebők-Nagy ◽  
László Biczók ◽  
Akimitsu Morimoto ◽  
Tetsuya Shimada ◽  
Haruo Inoue
Keyword(s):  
Energy Dissipation ◽  
Dissipation Processes ◽  
Hydrogen Bonded
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