Circuit Connectivity Inspired Neural Network for Analog Mixed-Signal Functional Modeling

2021 ◽  
Author(s):  
Mohsen Hassanpourghadi ◽  
Shiyu Su ◽  
Rezwan A Rasul ◽  
Juzheng Liu ◽  
Qiaochu Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Functional Modeling ◽  
Mixed Signal

scholarly journals A neuromorphic systems approach to in-memory computing with non-ideal memristive devices: from mitigation to exploitation

2019 ◽  
Vol 213 ◽  
pp. 487-510 ◽  
Author(s):  
Melika Payvand ◽  
Manu V. Nair ◽  
Lorenz K. Müller ◽  
Giacomo Indiveri
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Systems Approach ◽  
Spiking Neural Network ◽  
Neural Network Architecture ◽  
Mixed Signal ◽  
Neuromorphic Systems

In this paper, we present a spiking neural network architecture that supports the use of non-ideal memristive devices as synaptic elements and propose mixed-signal analog-digital interfacing circuits to mitigate/exploit such non-idealities for neuromorphic computation.

scholarly journals Digital image recognition based on improved cognitive neural network

2019 ◽  
Vol 10 (1) ◽  
pp. 125-128 ◽  
Author(s):  
Yuxi Liu
Keyword(s):  
Neural Network ◽  
Image Recognition ◽  
Digital Image ◽  
High Accuracy ◽  
Not Given ◽  
Mixed Signal ◽  
Neural Network Method ◽  
Innovative Method ◽  
Network Method ◽  
Source Signal

Abstract This paper presents an innovative cognitive neural network method application in digital image recognition. The following conclusion can be drawn. Each point of the graph is transformed, and the original color of the transformed new coordinates is given to the point. If after all the points have transformed, if there is a point and no point has converted to this point, the point is not given a color. Then this point will form a hole or a stripe, and the color is the color of the point initialization. The innovative method can effectively separate the digital image recognition signal from the mixed signal and maintain the waveform of the source signal with high accuracy, thus laying the foundation for the next step of recognition.

NEURAL NETWORK INTEGRATED CIRCUITS WITH SINGLE-BLOCK MIXED SIGNAL ARRAYS

1998 ◽  
Vol 08 (05n06) ◽  
pp. 589-604
Author(s):  
HORMOZ DJAHANSHAHI ◽  
MAJID AHMADI ◽  
GRAHAM A. JULLIEN ◽  
WILLIAM C. MILLER
Keyword(s):  
Neural Network ◽  
Integrated Circuits ◽  
Circuit Design ◽  
Supply Voltage ◽  
Network Size ◽  
Mixed Signal ◽  
Block Form ◽  
Single Block ◽  
Application Specific ◽  
Regular Arrays

This paper discusses the design and implementation of a family of mixed-signal neural network integrated circuits for general and application-specific purposes. Regular Arrays of a nonlinearly-loaded multiplier block form the core of multi-layer neural networks. Input-output circuitry and network size, however, vary depending on design applications. Salient features of the present architecture, such as modularity and reduced interconnection problems and areas are highlighted and circuit design and improvements are presented for its universal building block. Other design issues such as supply voltage and power reduction and pin limitations are discussed together with experimental results.

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