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Memristance Drift Avoidance with Charge Bouncing for Memristor-based Nonvolatile Memories
Shyam Prasad Adhikari,김형석,공배선,Leon O. Chua 한국물리학회 2012 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.61 No.9
A charge bouncing solution to avoid the undesirable drift in the programmed memory during readout of memristor-based non-volatile memory is proposed. Memristor memory can be programmed by strong programming signals, and the programmed memristance can be readout by weak readout signals. Though readout signals are weak compared to programming signals, readout charge is accumulated over time and leads to an undesirable drift of the operating point. This causes error in the programmed memory. Memristance drift is an important problem for practical utilization of memristors as memory. The only way presented so far to avoid drift is by converting input signals to doublets but un-ideal doublet signals still cause drifting problem. In the proposed method, drift is avoided with a capacitor in such a way that all the charge injected to the memristor during readout is stored in a capacitor, and bounced back through the memristor after completing the readout. Experimental results showing an excellent recovery from the temporal memristance drift using singlet pulses rather than the conventionally used doublet pulses for memristive memory readout are also presented.
Sah, Maheshwar Pd,Hyongsuk Kim,Chua, Leon O. IEEE 2014 IEEE circuits and systems magazine Vol.14 No.1
<P>This exposition shows that the potassium ion-channels and the sodium ion-channels that are distributed over the entire length of the axons of our neurons are in fact locally-active memristors. In particular, they exhibit all of the fingerprints of memristors, including the characteristic pinched hysteresis Lissajous figures in the voltage-current plane, whose loop areas shrink as the frequency of the periodic excitation signal increases. Moreover, the pinched hysteresis loops for the potassium ion-channel memristor, and the sodium ion-channel memristor, from the Hodgkin-Huxley axon circuit model are unique for each periodic excitation signal. An in-depth circuit-theoretic analysis and characterizations of these two classic biological memristors are presented via their small-signal memristive equivalent circuits, their frequency response, and their Nyquist plots. Just as the Hodgkin-Huxley circuit model has stood the test of time, its constituent potassium ion-channel and sodium ion-channel memristors are destined to be classic examples of locally-active memristors in future textbooks on circuit theory and bio-physics.</P>
Building cellular neural network templates with a hardware friendly learning algorithm
Adhikari, Shyam Prasad,Kim, Hyongsuk,Yang, Changju,Chua, Leon O. Elsevier 2018 Neurocomputing Vol.312 No.-
<P><B>Abstract</B></P> <P>A general solution for the construction of Cellular Neural Network (CNN) weights (cloning template) with Random Weight Change (RWC) algorithm is proposed. A target image for each input image is prepared via a sketch or any other kind of image processing technique for learning of Cellular Neural Network templates. A vector of randomly generated small values is added to the original weights and tested upon the input-target image pair. As a result, if the learning error decreases, the weight is taken for learning in the next iteration and updated using the same vector of random values. Otherwise, a new random vector for updating the weights is regenerated. One of the strong benefits of the proposed weight learning method is the simplicity of its learning algorithm and hence a simpler hardware architecture. Moreover the proposed method provides a unified solution to the problem of learning CNN templates without having to modify the original CNN structure and is applicable for all types of CNNs and input images. Successful learning of templates for various image processing tasks using different CNN structures are also demonstrated in this paper.</P>
Neural Synaptic Weighting With a Pulse-Based Memristor Circuit
Hyongsuk Kim,Sah, Maheshwar,Changju Yang,Roska, Tamá,s,Chua, Leon O. IEEE 2012 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS PART 1 R Vol.59 No.1
<P>A pulse-based programmable memristor circuit for implementing synaptic weights for artificial neural networks is proposed. In the memristor weighting circuit, both positive and negative multiplications are performed via a charge-dependent Ohm's law (). The circuit is composed of five memristors with bridge-like connections and operates like an artificial synapse with pulse-based processing and adjustability. The sign switching pulses, weight setting pulses and synaptic processing pulses are applied through a shared input terminal. Simulations are done with both linear memristor and window-based nonlinear memristor models.</P>
A Circuit-Based Learning Architecture for Multilayer Neural Networks With Memristor Bridge Synapses
Adhikari, Shyam Prasad,Hyongsuk Kim,Budhathoki, Ram Kaji,Changju Yang,Chua, Leon O. IEEE 2015 IEEE Transactions on Circuits and Systems I: Regul Vol.62 No.1
<P>Memristor-based circuit architecture for multilayer neural networks is proposed. It is a first of its kind demonstrating successful circuit-based learning for multilayer neural network built with memristors. Though back-propagation algorithm is a powerful learning scheme for multilayer neural networks, its hardware implementation is very difficult due to complexities of the neural synapses and the operations involved in the learning algorithm. In this paper, the circuit of a multilayer neural network is designed with memristor bridge synapses and the learning is realized with a simple learning algorithm called Random Weight Change (RWC). Though RWC algorithm requires more iterations than back-propagation algorithm, we show that a circuit-based learning using RWC is two orders faster than its software counterpart. The method to build a multilayer neural network using memristor bridge synapses and a circuit-based learning architecture of RWC algorithm is proposed. Comparison between software-based and memristor circuit-based learning are presented via simulations.</P>