Sierpinski gaskets for logic functions representation

<div>This paper presents the first experimental demonstration</div><div>of a ternary memristor-CMOS logic family. We systematically</div><div>design, simulate and experimentally verify the primitive</div><div>logic functions: the ternary AND, OR and NOT gates. These are then used to build combinational ternary NAND, NOR, XOR and XNOR gates, as well as data handling ternary MAX and MIN gates. Our simulations are performed using a 50-nm process which are verified with in-house fabricated indium-tin-oxide memristors, optimized for fast switching, high transconductance, and low current leakage. We obtain close to an order of magnitude improvement in data density over conventional CMOS logic, and a reduction of switching speed by a factor of 13 over prior state-of-the-art ternary memristor results. We anticipate extensions of this work can realize practical implementation where high data density is of critical importance.</div>

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On representation of k-valued logic functions by a sum of products of subfunctions

Discrete Mathematics and Applications ◽

10.1515/dma.2007.024 ◽

2007 ◽

Vol 17 (3) ◽

Author(s):

V. I. Panteleev ◽

N. A. Peryazev

Keyword(s):

Sum Of Products ◽

Logic Functions

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Realization of Logic Functions Using Switching Lattices Under a Delay Constraint

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems ◽

10.1109/tcad.2020.3035629 ◽

2020 ◽

pp. 1-1

Author(s):

Levent Aksoy ◽

Nihat Akkan ◽

Herman Sedef ◽

Mustafa Altun

Keyword(s):

Delay Constraint ◽

Logic Functions

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Skyrmion Logic-In-Memory Architecture for Maximum/Minimum Search

Electronics ◽

10.3390/electronics10020155 ◽

2021 ◽

Vol 10 (2) ◽

pp. 155

Author(s):

Luca Gnoli ◽

Fabrizio Riente ◽

Marco Vacca ◽

Massimo Ruo Roch ◽

Mariagrazia Graziano

Keyword(s):

High Efficiency ◽

Digital Design ◽

Control Logic ◽

Minimum Number ◽

Physical Simulations ◽

Good Energy ◽

Logic Functions ◽

Time And Energy ◽

Cmos Implementation ◽

Maximum Minimum

In modern computing systems there is the need to utilize a large amount of data in maintaining high efficiency. Limited memory bandwidth, coupled with the performance gap between memory and logic, impacts heavily on algorithms performance, increasing the overall time and energy required for computation. A possible approach to overcome such limitations is Logic-In-Memory (LIM). In this paper, we propose a LIM architecture based on a non-volatile skyrmion-based recetrack memory. The architecture can be used as a memory or can perform advanced logic functions on the stored data, for example searching for the maximum/minimum number. The circuit has been designed and validated using physical simulations for the memory array together with digital design tools for the control logic. The results highlight the small area of the proposed architecture and its good energy efficiency compared with a reference CMOS implementation.

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Training an asymmetric signal perceptron through reinforcement in an artificial chemistry

Journal of The Royal Society Interface ◽

10.1098/rsif.2013.1100 ◽

2014 ◽

Vol 11 (93) ◽

pp. 20131100 ◽

Cited By ~ 14

Author(s):

Peter Banda ◽

Christof Teuscher ◽

Darko Stefanovic

Keyword(s):

State Of The Art ◽

Chemical Species ◽

Mass Action ◽

Mass Action Kinetics ◽

Dna Strand Displacement ◽

Autonomous Adaptation ◽

Biochemical Systems ◽

Dna Strand ◽

Logic Functions ◽

Application Specific

State-of-the-art biochemical systems for medical applications and chemical computing are application-specific and cannot be reprogrammed or trained once fabricated. The implementation of adaptive biochemical systems that would offer flexibility through programmability and autonomous adaptation faces major challenges because of the large number of required chemical species as well as the timing-sensitive feedback loops required for learning. In this paper, we begin addressing these challenges with a novel chemical perceptron that can solve all 14 linearly separable logic functions. The system performs asymmetric chemical arithmetic, learns through reinforcement and supports both Michaelis–Menten as well as mass-action kinetics. To enable cascading of the chemical perceptrons, we introduce thresholds that amplify the outputs. The simplicity of our model makes an actual wet implementation, in particular by DNA-strand displacement, possible.

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