Related papers: Exploiting Dual-Gate Ambipolar CNFETs for Scalable…

On-chip learning in a crossbar array based analog hardware Neural Network (NN) has been shown to have major advantages in terms of speed and energy compared to training NN on a traditional computer. However analog hardware NN proposals and…

Electrostatically Formed Nanowire (EFN) based transistors have been suggested in the past as gas sensing devices. These transistors are multiple gate transistors in which the source to drain conduction path is determined by the bias applied…

Nano-electronic integrated circuit technology is exclusively based on MOSFET transistor due to its scalability down to the nanometer range. On the other hand, Bipolar Junction Transistor (BJT), which provides unmatched analog…

We extend our previous work on Inductive Conformal Prediction (ICP) for multi-label text classification and present a novel approach for addressing the computational inefficiency of the Label Powerset (LP) ICP, arrising when dealing with a…

This article proposes a general approach to the simulation and design of a multilayer perceptron (MLP) network on the basis of cross-bar arrays of metal-oxide memristive devices. The proposed approach uses the ANNM theory, tolerance theory,…

Data-intensive computing tasks, such as training neural networks, are crucial for artificial intelligence applications but often come with high energy demands. One promising solution is to develop specialized hardware that directly maps…

We fabricated ambipolar field-effect transistors (FETs) from multi-layered triclinic ReSe2, mechanically exfoliated onto a SiO2 layer grown on p-doped Si. In contrast to previous reports on thin layers (~2 to 3 layers), we extract…

Convolution Neural Networks (CNN) have performed well in many applications such as object detection, pattern recognition, video surveillance and so on. CNN carryout feature extraction on labelled data to perform classification. Multi-label…

Multi-Stage Classifier (MSC) - several classifiers working sequentially in an arranged order and classification decision is partially made at each step - is widely used in industrial applications for various resource limitation reasons. The…

We use an Atomic Force Microscope (AFM) tip to locally probe the electronic properties of semiconducting carbon nanotube transistors. A gold-coated AFM tip serves as a voltage or current probe in three-probe measurement setup. Using the tip…

The thesis investigates the utilization of memristive and memcapacitive crossbar arrays in low-power machine learning accelerators, offering a comprehensive co-design framework for deep neural networks (DNN). The model, implemented through…

Traditional transistors based on complementary metal-oxide-semiconductor (CMOS) and metal-oxide-semiconductor field-effect transistors (MOSFETs) are facing significant limitations as device scaling reaches the limits of Moore's Law. These…

Deep neural networks have revolutionized the field of machine learning by providing unprecedented human-like performance in solving many real-world problems such as image and speech recognition. Training of large DNNs, however, is a…

We present a simple and scalable technique for the fabrication of solution processed & local gated carbon nanotube field effect transistors (CNT-FETs). The approach is based on directed assembly of individual single wall carbon nanotube…

This letter proposes an in-sensor computing multiply-and-accumulate (MAC) circuit based on capacitance. The MAC circuits can constitute an artificial neural network(ANN) layer and be operated as ANN classifiers and autoencoders. The…

Microelectromechanical system (MEMS) based on-chip resonators offer great potential for high frequency signal processing circuits like reference oscillators and filters. This is due to their exceptional features like small size, large…

We propose and numerically simulate novel reconfigurable logic gates employing spin metal-oxide-semiconductor field-effect transistors (spin MOSFETs). The output characteristics of the spin MOSFETs depend on the relative magnetization…

Carbon nanotube field-effect transistors (CNFETs) are promising candidates for building energy-efficient digital systems at highly-scaled technology nodes. However, carbon nanotubes (CNTs) are inherently subject to variations that reduce…

A switched-capacitor matrix multiplier is presented for approximate computing and machine learning applications. The multiply-and-accumulate operations perform discrete-time charge-domain signal processing using passive switches and 300 aF…

The goal of this paper is to design image classification systems that, after an initial multi-task training phase, can automatically adapt to new tasks encountered at test time. We introduce a conditional neural process based approach to…