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

scholarly journals Stochastic Computing Implementation of Chaotic Systems

2021 ◽  
Author(s):  
Oscar Camps ◽  
Stavros G. Stavrinides ◽  
Rodrigo Picos
Keyword(s):  
Low Power ◽  
Data Transmission ◽  
Chaotic Systems ◽  
Edge Computing ◽  
The Other ◽  
Low Area ◽  
Stochastic Computing ◽  
Key Points ◽  
Secure Data Transmission ◽  
Chaotic Circuits

An exploding demand for processing capabilities related to the emergence of the IoT, AI and big data, has led to the quest for increasingly efficient ways to expeditiously process the rapidly increasing amount of data. These ways include different approaches like improved devices capable of going further in the more Moore path, but also new devices and architectures capable of going beyond Moore and getting more than Moore. Among the solutions being proposed, Stochastic Computing has positioned itself as a very reasonable alternative for low-power, low-area, low-speed, and adjustable precision calculations; four key-points beneficial to edge computing. On the other hand, chaotic circuits and systems appear to be an attractive solution for (low-power, green) secure data transmission in the frame of edge computing and IoT in general. Classical implementations of this class of circuits require intensive and precise calculations. This paper discusses the use of the SC framework for the implementation of nonlinear systems, showing that it can provide results comparable to those of classical integration, with much simpler hardware, paving the way for relevant applications.

scholarly journals Stochastic Computing Implementation of Chaotic Systems

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 375
Author(s):  
Oscar Camps ◽  
Stavros G. Stavrinides ◽  
Rodrigo Picos
Keyword(s):  
Low Power ◽  
Data Transmission ◽  
Chaotic Systems ◽  
Edge Computing ◽  
Low Area ◽  
Stochastic Computing ◽  
Key Points ◽  
Secure Data Transmission ◽  
Chaotic Circuits ◽  
The Internet Of Things

An exploding demand for processing capabilities related to the emergence of the Internet of Things (IoT), Artificial Intelligence (AI), and big data, has led to the quest for increasingly efficient ways to expeditiously process the rapidly increasing amount of data. These ways include different approaches like improved devices capable of going further in the more Moore path but also new devices and architectures capable of going beyond Moore and getting more than Moore. Among the solutions being proposed, Stochastic Computing has positioned itself as a very reasonable alternative for low-power, low-area, low-speed, and adjustable precision calculations—four key-points beneficial to edge computing. On the other hand, chaotic circuits and systems appear to be an attractive solution for (low-power, green) secure data transmission in the frame of edge computing and IoT in general. Classical implementations of this class of circuits require intensive and precise calculations. This paper discusses the use of the Stochastic Computing (SC) framework for the implementation of nonlinear systems, showing that it can provide results comparable to those of classical integration, with much simpler hardware, paving the way for relevant applications.

Compact and accurate digital filters based on stochastic computing

2019 ◽  
Vol 7 (1) ◽  
pp. 31-43 ◽  
Author(s):  
Hideyuki Ichihara ◽  
Tatsuyoshi Sugino ◽  
Shota Ishii ◽  
Tsuyoshi Iwagaki ◽  
Tomoo Inoue
Keyword(s):  
Digital Filters ◽  
Stochastic Computing

Implementation of digital filters via VLSI array processors

1988 ◽  
Vol 135 (2) ◽  
pp. 78
Author(s):  
B.G. Mertzios ◽  
V.L. Syrmos
Keyword(s):  
Digital Filters ◽  
Array Processors ◽  
Vlsi Array
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