A Proposal for Energy-Efficient Cellular Neural Network Based on Spintronic Devices

Energy-Efficient Network Memory for Ubiquitous Devices

This paper explores the energy and delay issues that occur when some or all of the local storage is moved out of the embedded device, and into a remote network server. We demonstrate using the network to access remote storage in lieu of local DRAM results in significant power savings. Mobile applications continually demand additional memory, with traditional designs increasing DRAM to address t...

Hybrid Spintronic-CMOS Spiking Neural Network With On-Chip Learning: Devices, Circuits and Systems

Over the past decade Spiking Neural Networks (SNN) have emerged as one of the popular architectures to emulate the brain. In SNN, information is temporally encoded and communication between neurons is accomplished by means of spikes. In such networks, spike-timing dependent plasticity mechanisms require the online programming of synapses based on the temporal information of spikes transmitted b...

Flexible spintronic devices on Kapton

A Radon-based Convolutional Neural Network for Medical Image Retrieval

Image classification and retrieval systems have gained more attention because of easier access to high-tech medical imaging. However, the lack of availability of large-scaled balanced labelled data in medicine is still a challenge. Simplicity, practicality, efficiency, and effectiveness are the main targets in medical domain. To achieve these goals, Radon transformation, which is a well-known t...

A conjugate gradient based method for Decision Neural Network training

Decision Neural Network is a new approach for solving multi-objective decision-making problems based on artificial neural networks. Using inaccurate evaluation data, network training has improved and the number of educational data sets has decreased. The available training method is based on the gradient decent method (BP). One of its limitations is related to its convergence speed. Therefore,...