Camps Oscar, Al Chawa Mohamad Moner, Stavrinides Stavros G, Picos Rodrigo
Industrial Engineering and Construction Department, University of Balearic Islands, 07122 Palma Mallorca, Spain.
Institute of Circuits and Systems, Technical University of Dresden, 01062 Dresden, Germany.
Micromachines (Basel). 2021 Dec 31;13(1):67. doi: 10.3390/mi13010067.
Cellular Nonlinear Networks (CNN) are a concept introduced in 1988 by Leon Chua and Lin Yang as a bio-inspired architecture capable of massively parallel computation. Since then, CNN have been enhanced by incorporating designs that incorporate memristors to profit from their processing and memory capabilities. In addition, Stochastic Computing (SC) can be used to optimize the quantity of required processing elements; thus it provides a lightweight approximate computing framework, quite accurate and effective, however. In this work, we propose utilization of SC in designing and implementing a memristor-based CNN. As a proof of the proposed concept, an example of application is presented. This application combines Matlab and a FPGA in order to create the CNN. The implemented CNN was then used to perform three different real-time applications on a 512 × 512 gray-scale and a 768 × 512 color image: storage of the image, edge detection, and image sharpening. It has to be pointed out that the same CNN was used for the three different tasks, with the sole change of some programmable parameters. Results show an excellent capability with significant accompanying advantages, such as the low number of needed elements further allowing for a low cost FPGA-based system implementation, something confirming the system's capacity for real time operation.
细胞非线性网络（CNN）是1988年由蔡少棠和杨琳提出的一个概念，它是一种受生物启发的架构，能够进行大规模并行计算。从那时起，通过纳入包含忆阻器的设计以利用其处理和存储能力，CNN得到了增强。此外，随机计算（SC）可用于优化所需处理元件的数量；然而，它提供了一个轻量级的近似计算框架，相当准确且有效。在这项工作中，我们提议在设计和实现基于忆阻器的CNN时利用SC。作为所提概念的一个证明，给出了一个应用示例。该应用结合了Matlab和一个FPGA来创建CNN。然后，所实现的CNN被用于在一幅512×512灰度图像和一幅768×512彩色图像上执行三种不同的实时应用：图像存储、边缘检测和图像锐化。必须指出的是，同一个CNN被用于这三个不同的任务，只是改变了一些可编程参数。结果显示出优异的能力以及显著的伴随优势，比如所需元件数量少，这进一步允许基于低成本FPGA的系统实现，这证实了该系统的实时操作能力。
