Memristor-Based Neural Logic Blocks for Nonlinearly Separable Functions

Soltiz, Michael; Kudithipudi, Dhireesha; Merkel, Cory; Rose, Garrett S.; Pino, Robinson E.

doi:10.1109/tc.2013.75

Cited by 37 publications

(12 citation statements)

References 21 publications

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“…In addition to the application in logic‐in‐memory computing, the XOR and XNOR logic opertations also play a significant role in pattern recognition systems . XOR and XNOR are not linearly separable with multiple decision boundaries, which makes XOR and XNOR logic operations regard as matching.…”

Section: Resultsmentioning

confidence: 99%

A hybrid memristor-CMOS XOR gate for nonvolatile logic computation

Zhou

et al. 2015

Phys. Status Solidi A

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Memristor-based logic gates that can execute memory and logic operations are promising elements for building non-von Neumann computation architecture. In this paper, we proposed a fundamental hybrid memristor-CMOS nonvolatile XOR Boolean logic block with one memristor and four voltagecontrolled switches, such as MOSFET or transmission gate. The Ag/Ag 5 In 5 Sb 60 Te 30 /Ta structure memristors with bipolar resistive switching behaviors were utilized to experimentally demonstrate the XOR function of the block. Further, with the XOR logic block as a basis, a full adder as an extensive circuit example was designed and verified by HSPICE simulation. Our work shows logic-in-memory capabilities with memristors, and provides an alternative way to implement nonvolatile logic operations and construct parallel computation systems.

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Section: Resultsmentioning

confidence: 99%

A hybrid memristor-CMOS XOR gate for nonvolatile logic computation

Zhou

et al. 2015

Phys. Status Solidi A

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“…RANLB, MTNLB, and ANLB are three different implementations of our adaptive activation function NLB design. The MTNLB (multi-threshold NLB) design [26] outperforms the standard lookup table approach, as well as the threshold logic gate approach (with monotonic activation functions) in all cases. The improvement is attributed to the reduction in NLBs required for a specific function, since each NLB is able to compute both linearly separable and non-linearly separable functions.…”

Section: Perceptron Networkmentioning

confidence: 99%

Design of Neuromorphic Architectures with Memristors

Kudithipudi

Merkel

Soltiz

et al. 2013

Advances in Information Security

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“…R on and R off are the lowest and highest resistances of the memristor respectively. We adopt a piecewise linear memristor model [10]. The model function is as follows:…”

Section: Memristor Modelmentioning

confidence: 99%

An adaptive neural network A/D converter based on CMOS/memristor hybrid design

Wang

You

Liu

et al. 2014

IEICE Electron. Express

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A memristor is regarded as a promising device for modeling synapses in the realization of artificial neural systems for its nanoscale size, analog storage properties, low energy and non-volatility. In this letter, an adaptive T-Model neural network based on CMOS/memristor hybrid design is proposed to perform the analog-to-digital conversion without oscillations. The circuit is composed of CMOS neurons and memristor synapses. The A/D converter (ADC) is trained by the least mean square (LMS) algorithm. The conductance of the memristors can be adjusted to convert input voltages with different ranges, which makes the ADC flexible. Using memristors as synapses in neuromorphic circuits can potentially offer high density.

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Memristor-Based Neural Logic Blocks for Nonlinearly Separable Functions

Cited by 37 publications

References 21 publications

A hybrid memristor-CMOS XOR gate for nonvolatile logic computation

A hybrid memristor-CMOS XOR gate for nonvolatile logic computation

Design of Neuromorphic Architectures with Memristors

An adaptive neural network A/D converter based on CMOS/memristor hybrid design

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