Dissipation minimization of two-stage amplifier using deep learning

Abstract

Designing electrical circuits and devices is usually based on expertise in electronics and the thorough use of numerical software tools. This procedure can be time-consuming, and the designer has only one solution. This paper introduces a new approach focused on new concept design and optimization of specific circuits using symbolic expressions. The primary amplifier circuit, realized by a deep learning module, changes the value to reduce power dissipation. The control signal of the deep learning module is output from an amplifier that depends on the statistics of the input signal value.

Machine Learning Application to Brain Diseases Detection

“…Machine learning as a subfield of artificial intelligence solves the problems of classification, clustering, and forecasting complex algorithms and methods, with the ability to extract patterns from raw data [24][25][26]. Also, deep learning is used in linear systems analysis [27,28]. In most cases, machine learning uses supervised learning [29]; the computer learns from a set of input-output pairs, which are called labeled examples.…”