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Full-Text Articles in Engineering
Energy-Efficient Neuromorphic Architectures For Nuclear Radiation Detection Applications, Jorge I. Canales-Verdial, Jamison R. Wagner, Landon A. Schmucker, Mark Wetzel, Nathan J. Withers, Philippe Erol Proctor, Christof Teuscher, Multiple Additional Authors
Energy-Efficient Neuromorphic Architectures For Nuclear Radiation Detection Applications, Jorge I. Canales-Verdial, Jamison R. Wagner, Landon A. Schmucker, Mark Wetzel, Nathan J. Withers, Philippe Erol Proctor, Christof Teuscher, Multiple Additional Authors
Electrical and Computer Engineering Faculty Publications and Presentations
A comprehensive analysis and simulation of two memristor-based neuromorphic architectures for nuclear radiation detection is presented. Both scalable architectures retrofit a locally competitive algorithm to solve overcomplete sparse approximation problems by harnessing memristor crossbar execution of vector–matrix multiplications. The proposed systems demonstrate excellent accuracy and throughput while consuming minimal energy for radionuclide detection. To ensure that the simulation results of our proposed hardware are realistic, the memristor parameters are chosen from our own fabricated memristor devices. Based on these results, we conclude that memristor-based computing is the preeminent technology for a radiation detection platform.
Fast And Accurate Sparse Coding Of Visual Stimuli With A Simple, Ultra-Low-Energy Spiking Architecture, Walt Woods, Christof Teuscher
Fast And Accurate Sparse Coding Of Visual Stimuli With A Simple, Ultra-Low-Energy Spiking Architecture, Walt Woods, Christof Teuscher
Electrical and Computer Engineering Faculty Publications and Presentations
Memristive crossbars have become a popular means for realizing unsupervised and supervised learning techniques. Often, to preserve mathematical rigor, the crossbar itself is separated from the neuron capacitors. In this work, we sought to simplify the design, removing extraneous components to consume significantly lower power at a minimal cost of accuracy. This work provides derivations for the design of such a network, named the Simple Spiking Locally Competitive Algorithm, or SSLCA, as well as CMOS designs and results on the CIFAR and MNIST datasets. Compared to a non-spiking model which scored 33% on CIFAR-10 with a single-layer classifier, this hardware …
Memory And Information Processing In Recurrent Neural Networks, Alireza Goudarzi, Sarah Marzen, Peter Banda, Guy Feldman, Matthew R. Lakin, Christof Teuscher, Darko Stefanovic
Memory And Information Processing In Recurrent Neural Networks, Alireza Goudarzi, Sarah Marzen, Peter Banda, Guy Feldman, Matthew R. Lakin, Christof Teuscher, Darko Stefanovic
Electrical and Computer Engineering Faculty Publications and Presentations
Recurrent neural networks (RNN) are simple dynamical systems whose computational power has been attributed to their short-term memory. Short-term memory of RNNs has been previously studied analytically only for the case of orthogonal networks, and only under annealed approximation, and uncorrelated input. Here for the first time, we present an exact solution to the memory capacity and the task-solving performance as a function of the structure of a given network instance, enabling direct determination of the function-structure relation in RNNs. We calculate the memory capacity for arbitrary networks with exponentially correlated input and further related it to the performance of …
A Brief Review Of Speaker Recognition Technology, Clark D. Shaver, John M. Acken
A Brief Review Of Speaker Recognition Technology, Clark D. Shaver, John M. Acken
Electrical and Computer Engineering Faculty Publications and Presentations
This paper reviews the development of speaker recognition systems from pre-computing days to current trends. Advances in various sciences which have allowed autonomous speaker recognition systems to become a practical means of identity authentication are also reviewed.
Modeling And Experimental Demonstration Of A Hopfield Network Analog-To-Digital Converter With Hybrid Cmos/Memristor Circuits, Xinjie Guo, Farnood Merrikh-Bayat, Ligang Gao, Brian D. Hoskins, Fabien Alibart, Bernabe Linares-Barranco, Luke Theogarajan, Christof Teuscher, Dmitri B. Strukov
Modeling And Experimental Demonstration Of A Hopfield Network Analog-To-Digital Converter With Hybrid Cmos/Memristor Circuits, Xinjie Guo, Farnood Merrikh-Bayat, Ligang Gao, Brian D. Hoskins, Fabien Alibart, Bernabe Linares-Barranco, Luke Theogarajan, Christof Teuscher, Dmitri B. Strukov
Electrical and Computer Engineering Faculty Publications and Presentations
The purpose of this work was to demonstrate the feasibility of building recurrent artificial neural networks with hybrid complementary metal oxide semiconductor (CMOS)/memristor circuits. To do so, we modeled a Hopfield network implementing an analog-to-digital converter (ADC) with up to 8 bits of precision. Major shortcomings affecting the ADC's precision, such as the non-ideal behavior of CMOS circuitry and the specific limitations of memristors, were investigated and an effective solution was proposed, capitalizing on the in-field programmability of memristors. The theoretical work was validated experimentally by demonstrating the successful operation of a 4-bit ADC circuit implemented with discrete Pt/TiO2− …
Blackfingers A Sophisticated Hand Prothesis, Michele Folgheraiter, Giuseppina Gini, Marek Perkowski, Mikhail Pivtoraiko
Blackfingers A Sophisticated Hand Prothesis, Michele Folgheraiter, Giuseppina Gini, Marek Perkowski, Mikhail Pivtoraiko
Electrical and Computer Engineering Faculty Publications and Presentations
Our goal is to develop the low level control system for an artificial hand ”Blackfingers”. Blackfingers was thought to have two main applications: as humanoid robot’s hand or as a human hand prothesis. In this last application our intent is to realize a device more sophisticated respect the actual commerce prothesis. Also in our intention is to use some biological paradigms to create a human like reflex control easy to be interfaced also with the human nervous system. In this paper we illustrate the properties and the morphology of a human like neural reflex controller, used to set the stiffness …
Le Bio Wall : Un Tissue Informatique Pour Le Prototypage De Systèmes Bio-Inspirés, Andre Stauffer, Daniel Mange, Gianluca Tempesti, Christof Teuscher
Le Bio Wall : Un Tissue Informatique Pour Le Prototypage De Systèmes Bio-Inspirés, Andre Stauffer, Daniel Mange, Gianluca Tempesti, Christof Teuscher
Electrical and Computer Engineering Faculty Publications and Presentations
Dans cet article, nous décrivons le BioWall, un tissu informatique reconfigurable géant développé dans le but d’y implémenter des machines mettant en oeuvre les principes de notre projet Embryonique. Bien que ses dimensions et ses caractéristiques en font d’abord un objet de démonstration publique, le BioWall constitue également un outil de recherche précieux, du fait que sa faculté de reprogrammation et sa structure cellulaire s’adaptent parfaitement à l’implémentation de toutes sortes de systèmes bioinspirés. Pour illustrer ces capacités, nous décrivons un ensemble d’applications qui reflètent différentes sources d’inspiration biologique allant des systèmes biologiques ontogénétiques aux dispositifs évolutifs phylogénétiques, en passant …