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Proceedings Paper

FG-MOS neuron for binary CNN
Author(s): Jacek Flak; Mika Laiho; Kari Halonen
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Paper Abstract

This paper presents a neuron implementation based on floating-gate MOSFET (FG-MOS) structure. The computation is performed by charge distribution at the input of FG-MOS inverter determining the cell state. There is no current-flow through the interconnections after processing is completed, thus a significant reduction in DC power consumption can be achieved. Such neuron can be used to build a capacitively coupled cellular neural/nonlinear network (CNN) for processing black and white (B/W) images. Although the coupling coefficients are basically implemented with capacitances, this approach provides them with 1-bit programmability. Also the neuron's threshold level can be digitally programmed to four different values. The templates operating on the B/W images can be modified to have only binary-valued {0,1} terms or can be split into such (sequentially run) simple subtasks. Therefore, the presented neuron structure is able to perform the evaluation of almost all B/W templates proposed so far. Exploration of FG-MOS structures can help to understand the implementation problems of capacitively coupled CNNs. Such a situation appears, e.g., in nanoelectronic CNNs composed of single-electron tunneling (SET) transistors, which also deal with B/W images only. Moreover, the binary programmability approach utilized here should help to develop an effective programming scheme for future SET or CMOS-SET hybrid CNN implementations. Along with the neuron structure, its operation description and simulation results of the 8 x 8 network are presented.

Paper Details

Date Published: 29 June 2005
PDF: 9 pages
Proc. SPIE 5839, Bioengineered and Bioinspired Systems II, (29 June 2005); doi: 10.1117/12.608059
Show Author Affiliations
Jacek Flak, Helsinki Univ. of Technology (Finland)
Mika Laiho, Univ. of Turku (Finland)
Kari Halonen, Helsinki Univ. of Technology (Finland)

Published in SPIE Proceedings Vol. 5839:
Bioengineered and Bioinspired Systems II
Ricardo A. Carmona; Gustavo Linan-Cembrano, Editor(s)

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