Realization of the Conscience Mechanism in CMOS Implementation of Winner-Takes-All Self-Organizing Neural Networks

This paper presents a complementary metal–oxide– semiconductor (CMOS) implementation of a conscience mechanism used to improve the effectiveness of learning in the winnertakes- all (WTA) artificial neural networks (ANNs) realized at the transistor level. This mechanism makes it possible to eliminate the effect of the so-called “dead neurons,” which do not take part in the learning phase competition. These neurons usually have a detrimental effect on the network performance, increasing the quantization error. The proposed mechanism comes as part of the analog implementation of the WTA neural networks (NNs) designed for applications to ultralow power portable diagnostic devices for online analysis of ECGbiomedical signals. The study presents Matlab simulations of the network’s model, discusses postlayout circuit level simulations and includes results of measurement completed for the physical realization of the circuit

Single Electron Devices and Circuit Architectures: Modeling Techniques, Dynamic Characteristics, and Reliability Analysis

The Single Electron (SE) technology is an important approach to enabling further feature size reduction and circuit performance improvement. However, new methods are required for device modeling, circuit behavior description, and reliability analysis with this technology due to its unique operation mechanism. In this thesis, a new macro-model of SE turnstile is developed to describe its physical characteristics for large-scale circuit simulation and design. Based on this model, several novel circuit architectures are proposed and implemented to further demonstrate the advantages of SE technique. The dynamic behavior of SE circuits, which is different from their CMOS counterpart, is also investigated using a statistical method. With the unreliable feature of SE devices in mind, a fast and recursive algorithm is developed to evaluate the reliability of SE logic circuits in a more efficient and effective manner

A Flexible, Low-Power, Programmable Unsupervised Neural Network Based on Microcontrollers for Medical Applications

We present an implementation and laboratory tests of a winner takes all (WTA) artificial neural network (NN) on two microcontrollers (μC) with the ARM Cortex M3 and the AVR cores. The prospective application of this device is in wireless body sensor network (WBSN) in an on-line analysis of electrocardiograph (ECG) and electromyograph (EMG) biomedical signals. The proposed device will be used as a base station in the WBSN, acquiring and analysing the signals from the sensors placed on the human body. The proposed system is equiped with an analog-todigital converter (ADC), and allows for multi-channel acquisition of analog signals, preprocessing (filtering) and further analysis