Mathematical simulation of memristive for classification in machine learning

Over the last few years, neuromorphic computation has been a widely researched topic. One of the neuromorphic computation elements is the memristor. The memristor is a high density, analogue memory storage, and compliance with Ohm's law for minor potential changes. Memristive behaviour imitates synaptic behaviour. It is a nanotechnology that can reduce power consumption, improve synaptic modeling, and reduce data transmission processes. The purpose of this paper is to investigate a customized mathematical model for machine learning algorithms. This model uses a computing paradigm that differs from standard Von-Neumann architectures, and it has the potential to reduce power consumption and increasing performance while doing specialized jobs when compared to regular computers. Classification is one of the most interesting fields in machine learning to classify features patterns by using a specific algorithm. In this study, a classifier based memristive is used with an adaptive spike encoder for input data. We run this algorithm based on Anti-Hebbian and Hebbian learning rules. These investigations employed two of datasets, including breast cancer Wisconsin and Gaussian mixture model datasets. The results indicate that the performance of our algorithm that has been used based on memristive is reasonably close to the optimal solution

Memristors for the Curious Outsiders

We present both an overview and a perspective of recent experimental advances and proposed new approaches to performing computation using memristors. A memristor is a 2-terminal passive component with a dynamic resistance depending on an internal parameter. We provide an brief historical introduction, as well as an overview over the physical mechanism that lead to memristive behavior. This review is meant to guide nonpractitioners in the field of memristive circuits and their connection to machine learning and neural computation.Comment: Perpective paper for MDPI Technologies; 43 page

Resistive switching in ALD metal-oxides with engineered interfaces

L'abstract è presente nell'allegato / the abstract is in the attachmen

Applications and Techniques for Fast Machine Learning in Science

In this community review report, we discuss applications and techniques for fast machine learning (ML) in science - the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs