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Full-Text Articles in Engineering
Architectures And Algorithms For Intrinsic Computation With Memristive Devices, Jens Bürger
Architectures And Algorithms For Intrinsic Computation With Memristive Devices, Jens Bürger
Dissertations and Theses
Neuromorphic engineering is the research field dedicated to the study and design of brain-inspired hardware and software tools. Recent advances in emerging nanoelectronics promote the implementation of synaptic connections based on memristive devices. Their non-volatile modifiable conductance was shown to exhibit the synaptic properties often used in connecting and training neural layers. With their nanoscale size and non-volatile memory property, they promise a next step in designing more area and energy efficient neuromorphic hardware.
My research deals with the challenges of harnessing memristive device properties that go beyond the behaviors utilized for synaptic weight storage. Based on devices that exhibit …
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 …
Sparse Adaptive Local Machine Learning Algorithms For Sensing And Analytics, Jack Cannon
Sparse Adaptive Local Machine Learning Algorithms For Sensing And Analytics, Jack Cannon
Undergraduate Research & Mentoring Program
The goal of digital image processing is to capture, transmit, and display images as efficiently as possible. Such tasks are computationally intensive because an image is digitally represented by large amounts of data. It is possible to render an image by reconstructing it with a subset of the most relevant data. One such procedure used to accomplish this task is commonly referred to as sparse coding. For our purpose, we use images of handwritten digits that are presented to an artificial neural network. The network implements Rozell's locally competitive algorithm (LCA) to generate a sparse code. This sparse code is …
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.