CVNS-Based Storage and Refreshing Scheme for a Multi-Valued Dynamic Memory

Khodabandehloo, Golnar; Mirhassani, Mitra; Ahmadi, M.

doi:10.1109/tvlsi.2010.2048588

Cited by 9 publications

(4 citation statements)

References 14 publications

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“…The acceptable resolution for 0.18µm CMOS technology using a power supply voltage of 1.8V is 4-bit [5].…”

Section: Truncated Cvns Distributed Neural Networkmentioning

confidence: 99%

“…The following equation shows CVNS digit generation from binary bits with group length of 4 and digit link of 1 [5]. (4) where w i shows the i th bit.…”

Section: Truncated Cvns Distributed Neural Networkmentioning

confidence: 99%

“…Truncation methods are applied to CVNS digits to make them cope with lower resolution environments while still keeping the CVNS properties [5], [6]. One of the advantages of applying these methods is reducing the area consumption; however, it may result in lower accuracy as well.…”

Section: Introductionmentioning

confidence: 99%

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A study on resistive-type truncated CVNS Distributed Neural Networks

Khodabandehloo

Mirhassani

Ahmadi

2011

2011 IEEE International Symposium of Circuits and Systems (ISCAS)

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Distributed Neural Networks (DNNs) are generally providing self-scaling property together with higher noise immunity for resistive-type neural networks. Continuous Valued Number System (CVNS) is a potential candidate to build the DNNs; however, implementation of a CVNS digit in its complete form needs a high resolution environment which is not practical. Truncation methods are applied to CVNS digits to make them adaptable to the low resolution environments. However, truncated CVNS operations may decrease the accuracy and immunity to noise compared to the complete CVNS operations. In this work, a truncated CVNS DNN is proposed, and studies over Noise to Signal Ratio (NSR) and accuracy are provided. Studies show that the accuracy is acceptable, and the NSR is still less than the NSR of conventional DNNs.

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“…The acceptable resolution for 0.18µm CMOS technology using a power supply voltage of 1.8V is 4-bit [5].…”

Section: Truncated Cvns Distributed Neural Networkmentioning

confidence: 99%

“…The following equation shows CVNS digit generation from binary bits with group length of 4 and digit link of 1 [5]. (4) where w i shows the i th bit.…”

Section: Truncated Cvns Distributed Neural Networkmentioning

confidence: 99%

See 1 more Smart Citation

A study on resistive-type truncated CVNS Distributed Neural Networks

Khodabandehloo

Mirhassani

Ahmadi

2011

2011 IEEE International Symposium of Circuits and Systems (ISCAS)

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“…The radix-2 CVNS arithmetic has been very effective and used in development of a series of arithmetic units [8][9][10][11]. This class of the CVNS can interface with the digital inputs directly, and the conversion process between the two number systems is very simple.…”

mentioning

confidence: 99%

Robust Analog Arithmetic Based on the Continuous Valued Number System

Zamanlooy

Mirhassani

2017

Embedded Systems Design With Special Arithmetic and Number Systems

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To implement arithmetic architectures there are many approaches available, some with new logic families such as Multiple-Valued-Logic (MVL) [1,2]. In this type of arithmetic systems, the signal levels in the digits are increased from a conventional two-state binary to multiple values. This feature is specially interesting for hardware implementation of neural networks, where a high number of interconnections are required between the network layers. Using a multiple-valued number system can reduce the number of interconnections, and hence reduce the area and power consumption of the circuit.The Continuous Valued Number System (CVNS) falls within the MVL arithmetic category. The CVNS and the basic concept of analog-digits were first introduced in [3], fundamental methods of addition arithmetic and array multiplication were developed in [4,5], and circuit design issues were discussed in [6] and [7].The radix-2 CVNS arithmetic has been very effective and used in development of a series of arithmetic units [8][9][10][11]. This class of the CVNS can interface with the digital inputs directly, and the conversion process between the two number systems is very simple. The radix-2 CVNS is also used for implementation of mixed-signal multipliers [12]. The multiplier is useful for implementation of high density CVNS neural networks, where a high number of adders and multipliers are required. The CVNS multiplier is compact, area efficient, and its operations are verified under different environmental conditions.

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