Implementing Boolean Functions in Hybrid Digital-Analog Systems

Kohar, Vivek; Kia, Behnam; Lindner, John F.; Ditto, William L.

doi:10.1103/physrevapplied.7.044006

Cited by 14 publications

(4 citation statements)

References 17 publications

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“…This rate is used for inference purposes as well. Recently, temporal information encoding is being actively explored in the domain of neuromorphic algorithms [8][9][10][11][12] while it has been long studied in the domain of stochastic non-linear systems [20][21][22] . The principal benefits of using temporal encoding for modelling spiking behavior are multiple.…”

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confidence: 99%

Stochastic magnetoelectric neuron for temporal information encoding

Yang

Sengupta

2020

Applied Physics Letters

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Emulating various facets of computing principles of the brain can potentially lead to the development of neurocomputers that are able to exhibit brain-like cognitive capabilities. In this letter, we propose a magnetoelectronic neuron that utilizes noise as a computing resource and is able to encode information over time through the independent control of external voltage signals. We extensively characterize the device operation using simulations and demonstrate its suitability for neuromorphic computing platforms performing temporal information encoding.

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confidence: 99%

Stochastic magnetoelectric neuron for temporal information encoding

Yang

Sengupta

2020

Applied Physics Letters

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“…a tiny change caused by a butterfly flapping its wings in one region leading to a drastic change of weather in another distant part of the globe. These two defining features, namely, deterministic aperiodicity and sensitive dependence on the initial state render chaotic systems suitable for numerous applications including, data and image encryption [3], [4], [5], pseudo-random number generation (PRNG) [6], [7], [8], dynamical system modeling [9], [10], reconfigurable logic [11], [12], side channel attack mitigation [13] [14], secure communication [15], [16], logic obfuscation [17], [18] and so on.…”

Section: Introductionmentioning

confidence: 99%

Design, Analysis, and Application of Flipped Product Chaotic System

et al. 2022

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In this paper, a novel method is proposed to build an improved 1-D discrete chaotic map called flipped product chaotic system (FPCS) by multiplying the output of one map with the output of a vertically flipped second map. Two variants, each with nine combinations, are shown with trade-off between computational cost and performance. The chaotic properties are explored using the bifurcation diagram, Lyapunov exponent, Kolmogorov entropy, and correlation coefficient. The proposed schemes offer a wider chaotic range and improved chaotic performance compared to the constituent maps and several prior works of similar nature. Wide chaotic window and improved chaotic complexity are two desired characteristics for several security applications as these two characteristics ensure enhanced design space with elevated entropic properties. We present a general Field-Programmable Gate Array (FPGA) design framework for the hardware implementation of the proposed flipped-product schemes and the results show good qualitative agreement with the numerical results from MATLAB simulation. Finally, we present a new Pseudo Random Number Generator (PRNG) using the two variants of the proposed chaotic map and validate their excellent randomness property using four standard statistical tests, namely NIST, FIPS, TestU01, and Diehard.INDEX TERMS Nonlinear dynamical systems, chaos, field-programmable gate arrays (FPGA), bifurcation diagram, Lyapunov exponent, discrete-time map, random number generation (RNG)

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“…Further, most of the previous studies have focused to produce OR(NOR) and AND(NAND) logic gates [8][9][10][11][12][24][25][26]31,32,48,49 . Obtaining Ex-OR (simply XOR) and Ex-NOR (XNOR) in dynamical systems are equally important since these gates are the basis of ubiquitous bit-by-bit addition 22 .…”

Section: Introductionmentioning

confidence: 99%

Realization of all logic gates and memory latch in the SC-CNN cell of the simple nonlinear MLC circuit

Ashokkumar,

Aravindh,

Venkatesan

et al. 2021

Preprint

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We investigate the State-Controlled Cellular Neural Network (SC-CNN) framework of Murali-Lakshmanan-Chua (MLC) circuit system subjected to two logical signals. By exploiting the attractors generated by this circuit in different regions of phase-space, we show that the nonlinear circuit is capable of producing all the logic gates, namely OR, AND, NOR, NAND, Ex-OR and Ex-NOR gates available in digital systems. Further the circuit system emulates three-input gates and Set-Reset flip-flop logic as well. Moreover, all these logical elements and flip-flop are found to be tolerant to noise. These phenomena are also experimentally demonstrated. Thus our investigation to realize all logic gates and memory latch in a nonlinear circuit system paves the way to replace or complement the existing technology with a limited number of hardware.

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Implementing Boolean Functions in Hybrid Digital-Analog Systems

Cited by 14 publications

References 17 publications

Stochastic magnetoelectric neuron for temporal information encoding

Stochastic magnetoelectric neuron for temporal information encoding

Design, Analysis, and Application of Flipped Product Chaotic System

Realization of all logic gates and memory latch in the SC-CNN cell of the simple nonlinear MLC circuit

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