Reliable and Energy-efficient Diabetic Retinopathy Screening using Memristor-based Neural Networks

Diabetic retinopathy (DR) is a leading cause of permanent vision loss worldwide. It refers to irreversible retinal damage caused due to elevated glucose levels and blood pressure. Regular screening for DR can facilitate its early detection and timely treatment. Neural network-based DR classifiers can be leveraged to achieve such screening in...

Editorial: Novel memristor-based devices and circuits for neuromorphic and AI applications

PetaOps/W edge-AI μ Processors: Myth or reality?

With the rise of deep learning (DL), our world braces for artificial intelligence (AI) in every edge device, creating an urgent need for edge-AI SoCs. This SoC hardware needs to support high throughput, reliable and secure AI processing at ultra-low power (ULP), with a very short time to market. With its strong legacy in edge solutions and open...

SparseMEM: Energy-efficient Design for In-memory Sparse-based Graph Processing

Performing analysis on large graph datasets in an energy-efficient manner has posed a significant challenge; not only due to excessive data movements and poor locality, but also due to the non-optimal use of high sparsity of such datasets. The latter leads to a waste of resources as the computation is also performed on zero's operands which do...

Dependability of Future Edge-AI Processors: Pandora’s Box

This paper addresses one of the directions of the HORIZON EU CONVOLVE project being dependability of smart edge processors based on computation-in-memory and emerging memristor devices such as RRAM. It discusses how how this alternative computing paradigm will change the way we used to do manufacturing test. In addition, it describes how these...

Application of Emerging Memory Technologies for Spiking Neural Networks

Renewed interest in memory technologies such as memristors and ferroelectric devices can provide opportunities for traditional and non-traditional computing systems alike. To make versatile, reprogrammable AI hardware possible, neuromorphic systems are in need of a low-power, non-volatile and analog memory solution to store the weights of the...

Dealing with Non-Idealities in Memristor Based Computation-In-Memory Designs

Computation-In-Memory (CIM) using memristor devices provides an energy-efficient hardware implementation of arithmetic and logic operations for numerous applications, such as neuromorphic computing and database query. However, memristor-based CIM suffers from various non-idealities such as conductance drift, read disturb, wire parasitics,...

MNEMOSENE: Tile Architecture and Simulator for Memristor-based Computation-in-memory

In recent years, we are witnessing a trend toward in-memory computing for future generations of computers that differs from traditional von-Neumann architecture in which there is a clear distinction between computing and memory units. Considering that data movements between the central processing unit (CPU) and memory consume several orders...

De memristor in feedback control systemen - The memristor in feedback control systems

In 2008 Hewlett-Packard requested a patent on a passive memristive device, which caused the memristor to become a popular area of research again. This thesis focuses on the mathematical derivation and definition of a memristor and how we can incorporate such a memristor in a feedback control system. We determine the describing function of a...

Memristors: One step closer towards electrical brains

The memristor is an electronic component which was invented in theory by Leon Chua in 1971, based on logics and symmetry. In 2008, HP Labs has realized an actual memristor on the nanoscale level for the first time in history. Since then, a lot of researchers have focused their attention on the memristor. It is one of the fundamental passive...