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Full-Text Articles in Computer Engineering
Vi Energy-Efficient Memristor-Based Neuromorphic Computing Circuits And Systems For Radiation Detection Applications, Jorge Iván Canales Verdial
Vi Energy-Efficient Memristor-Based Neuromorphic Computing Circuits And Systems For Radiation Detection Applications, Jorge Iván Canales Verdial
Electrical and Computer Engineering ETDs
Radionuclide spectroscopic sensor data is analyzed with minimal power consumption through the use of neuromorphic computing architectures. Memristor crossbars are harnessed as the computational substrate in this non-conventional computing platform and integrated with CMOS-based neurons to mimic the computational dynamics observed in the mammalian brain’s visual cortex. Functional prototypes using spiking sparse locally competitive approximations are presented. The architectures are evaluated for classification accuracy and energy efficiency. The proposed systems achieve a 90% true positive accuracy with a high-resolution detector and 86% with a low-resolution detector.
Improving The Efficiency Of Dnn Hardware Accelerator By Replacing Digitalfeature Extractor With An Imprecise Neuromorphic Hardware, Majid Mohammadi Rad, Omid Sojodishijani
Improving The Efficiency Of Dnn Hardware Accelerator By Replacing Digitalfeature Extractor With An Imprecise Neuromorphic Hardware, Majid Mohammadi Rad, Omid Sojodishijani
Turkish Journal of Electrical Engineering and Computer Sciences
Mixed-signal in-memory computation can drastically improve the efficiency of the hardware implementing machine learning (ML) algorithms by (i) removing the need to fetch neural network parameters from internal or external memory and (ii) performing a large number of multiply-accumulate operations in parallel. However, this boost in efficiency comes with some disadvantages. Among them, the inability to precisely program nonvolatile memory devices (NVM) with neural network parameters and sensitivity to noise prevent the mixed-signal hardware to perform a precise and deterministic computation. Unfortunately, these hardware-specific errors can get magnified while propagating along with the layers of the deep neural network. In …
Cmos Compatible Memristor Networks For Brain-Inspired Computing, Can Li
Cmos Compatible Memristor Networks For Brain-Inspired Computing, Can Li
Doctoral Dissertations
In the past decades, the computing capability has shown an exponential growth trend, which is observed as Moore’s law. However, this growth speed is slowing down in recent years mostly because the down-scaled size of transistors is approaching their physical limit. On the other hand, recent advances in software, especially in big data analysis and artificial intelligence, call for a break-through in computing hardware. The memristor, or the resistive switching device, is believed to be a potential building block of the future generation of integrated circuits. The underlying mechanism of this device is different from that of complementary metal-oxide-semiconductor (CMOS) …
Analog Signal Processing Solutions And Design Of Memristor-Cmos Analog Co-Processor For Acceleration Of High-Performance Computing Applications, Nihar Athreyas
Analog Signal Processing Solutions And Design Of Memristor-Cmos Analog Co-Processor For Acceleration Of High-Performance Computing Applications, Nihar Athreyas
Doctoral Dissertations
Emerging applications in the field of machine vision, deep learning and scientific simulation require high computational speed and are run on platforms that are size, weight and power constrained. With the transistor scaling coming to an end, existing digital hardware architectures will not be able to meet these ever-increasing demands. Analog computation with its rich set of primitives and inherent parallel architecture can be faster, more efficient and compact for some of these applications. The major contribution of this work is to show that analog processing can be a viable solution to this problem. This is demonstrated in the three …
Analog Computing Using 1t1r Crossbar Arrays, Yunning Li
Analog Computing Using 1t1r Crossbar Arrays, Yunning Li
Masters Theses
Memristor is a novel passive electronic device and a promising candidate for new generation non-volatile memory and analog computing. Analog computing based on memristors has been explored in this study. Due to the lack of commercial electrical testing instruments for those emerging devices and crossbar arrays, we have designed and built testing circuits to implement analog and parallel computing operations. With the setup developed in this study, we have successfully demonstrated image processing functions utilizing large memristor crossbar arrays. We further designed and experimentally demonstrated the first memristor based field programmable analog array (FPAA), which was successfully configured for audio …
Energetic Behavior Of Resistive Random-Access Memory Cells, Christopher M. Bisbee
Energetic Behavior Of Resistive Random-Access Memory Cells, Christopher M. Bisbee
Macalester Journal of Physics and Astronomy
In order to investigate the switching characteristics of Resistive Random Access Memory cells (ReRAM) in terms of their thermodynamic free energy properties, we need to build a number of models that replicate the system. This report contains the models used to investigate filament growth patterns based on different boundary conditions applied to the electrode-filament system. Using Comsol Multiphysics software, we determined that when a fixed voltage is applied to each electrode in the electrode-filament system, we should expect filament dissolution that resets our cells into the High Resistance State (HRS). If we instead fix a set amount of charge on …
Reconstructive Sensing Circuit For Complementary Resistive Switches-Based Crossbar Memories, Ertuğrul Karakulak, Reşat Mutlu, Erdem Uçar
Reconstructive Sensing Circuit For Complementary Resistive Switches-Based Crossbar Memories, Ertuğrul Karakulak, Reşat Mutlu, Erdem Uçar
Turkish Journal of Electrical Engineering and Computer Sciences
Complementary resistive switches (CRSs) are suggested as an alternative to one-cell memristor memories to decrease leakage currents. However, their sensing is more difficult and complex than one-cell memristor memories. A method has been given for sensing their state using only DC voltages in the literature. However, in this strategy, sensing one of the logic states results in the destruction of the state and the destroyed state must be written again. To the best of our knowledge, a circuit with this sensing strategy does not exist in the literature yet. In this paper, such a circuit employing this method, which is …
Behavior Characteristics Of A Cap-Resistor, Memcapacitor, And A Memristor From The Response Obtained Of Rc And Rl Electrical Circuits Described By Fractional Differential Equations, Jose Francisco Gomez Aguilar
Behavior Characteristics Of A Cap-Resistor, Memcapacitor, And A Memristor From The Response Obtained Of Rc And Rl Electrical Circuits Described By Fractional Differential Equations, Jose Francisco Gomez Aguilar
Turkish Journal of Electrical Engineering and Computer Sciences
This paper provides an analysis of RC and RL electrical circuits described by a fractional difierential equation of Caputo type. The order considered is ${0 \textless \gamma }\le ${ 1}. The Laplace transform of the fractional derivative is used. To keep the dimensionality of the physical quantities, R, C, L, and an auxiliary parameter $\sigma $ are introduced, characterizing the existence of fractional components in the system. The relationship between $\gamma$ and $\sigma $ is reported. The response obtained from the fractional RC and RL circuits exhibits the characteristic behaviors of a cap-resistor, memcapacitor, and memristor, as well as charge-voltage …