Stochastic Computing via In Operando Modulation of Rectification in Molecular Tunneling Junctions

2020 ◽  
Author(s):  
Xinkai Qiu ◽  
Sylvia Rousseva ◽  
Gang Ye ◽  
Jan C. Hummelen ◽  
Ryan Chiechi
Keyword(s):  
Self Assembly ◽  
Variable Temperature ◽  
Self Assembled Monolayers ◽  
Microfluidic Channels ◽  
Proof Of Concept ◽  
Glycol Ethers ◽  
Stochastic Computing ◽  
Assembled Monolayers ◽  
In Operando ◽  
Tunneling Junctions

This paper describes the reconfiguration of molecular tunneling junctions during operation via the self-assembly of bilayers of glycol ethers. We use well-established functional groups to modulate the magnitude and direction of rectification in assembled tunneling junctions by exposing them to solutions containing different glycol ethers. Variable-temperature measurements establish that rectification occurs by a bias-dependent tunneling-hopping mechanism and that glycol ethers, beside being an unusually efficient tunneling medium, behave identically to alkanes. We fabricated memory bits from crossbar junctions prepared by injecting eutectic Ga-In into microfluidic channels. Two 8-bit registers were able to perform logical AND operations on bit strings encoded into chemical packets as microfluidic droplets that alter the composition of the crossbar junctions through self-assembly to effect memristor-like properties. This proof of concept work demonstrates the potential for fieldable molecular-electronic devices based on tunneling junctions of self-assembled monolayers and bilayers.

scholarly journals A Hardware Pseudo-Random Number Generator Using Stochastic Computing and Logistic Map

Micromachines ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 31
Author(s):  
Junxiu Liu ◽  
Zhewei Liang ◽  
Yuling Luo ◽  
Lvchen Cao ◽  
Shunsheng Zhang ◽  
...  
Keyword(s):  
Random Number ◽  
Chaotic Map ◽  
Logistic Map ◽  
Random Number Generator ◽  
Alternative Methods ◽  
Random Numbers ◽  
Number Generator ◽  
Stochastic Computing ◽  
Pseudo Random Number ◽  
Pseudo Random Number Generator

Recent research showed that the chaotic maps are considered as alternative methods for generating pseudo-random numbers, and various approaches have been proposed for the corresponding hardware implementations. In this work, an efficient hardware pseudo-random number generator (PRNG) is proposed, where the one-dimensional logistic map is optimised by using the perturbation operation which effectively reduces the degradation of digital chaos. By employing stochastic computing, a hardware PRNG is designed with relatively low hardware utilisation. The proposed hardware PRNG is implemented by using a Field Programmable Gate Array device. Results show that the chaotic map achieves good security performance by using the perturbation operations and the generated pseudo-random numbers pass the TestU01 test and the NIST SP 800-22 test. Most importantly, it also saves 89% of hardware resources compared to conventional approaches.

Low-area and accurate inner product and digital filters based on stochastic computing

2021 ◽  
Vol 183 ◽  
pp. 108040
Author(s):  
Hamdan Abdellatef ◽  
Mohamed Khalil-Hani ◽  
Nasir Shaikh-Husin ◽  
Sayed Omid Ayat
Keyword(s):  
Digital Filters ◽  
Inner Product ◽  
Low Area ◽  
Stochastic Computing

Normalization and dropout for stochastic computing-based deep convolutional neural networks

Integration ◽  
2019 ◽  
Vol 65 ◽  
pp. 395-403 ◽  
Author(s):  
Ji Li ◽  
Zihao Yuan ◽  
Zhe Li ◽  
Ao Ren ◽  
Caiwen Ding ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Stochastic Computing
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