Area-efficient robust Madaline based on continuous valued number system

Achieving a low Noise-to-Signal Ratio (NSR) is one of the major concerns when implementing hardware-based neural networks. Continuous Valued Number System (CVNS) features have been exploited to improve the NSR. The efficiency of the network model in terms of area, power consumption, and NSR is measu...

Full description

Saved in:
Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2014-09, Vol.140, p.128-145
Main Authors: Zamanlooy, Babak, Mirhassani, Mitra
Format: Article
Language:English
Subjects:
Applied sciences
Arithmetic
Artificial intelligence
Computer science
control theory
systems
Connectionism. Neural networks
Continuous Valued Number System (CVNS)
Electronics
Exact sciences and technology
Integrated circuits
Integrated circuits by function (including memories and processors)
Mathematical analysis
Networks
Neural networks
Neurons
Noise-to-Signal Ratio (NSR)
Power consumption
Registers
Semiconductor electronics. Microelectronics. Optoelectronics. Solid state devices
Stores
VLSI implementation
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Achieving a low Noise-to-Signal Ratio (NSR) is one of the major concerns when implementing hardware-based neural networks. Continuous Valued Number System (CVNS) features have been exploited to improve the NSR. The efficiency of the network model in terms of area, power consumption, and NSR is measured using the product of the total number of neurons in the network multiplied by the network NSR, which indicates the number of neurons required for a specific NSR. The network proposed in this paper stores the weights in digital registers while the processing is done in the analog domain using CVNS arithmetic. The mathematical analysis and comparison between the proposed network and the previous structures prove that the proposed architecture improves upon in terms of both the NSR and the product of neuron multiplied by NSR.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2014.03.029