Abstract: A hardware accelerator including a crossbar array programmed to calculate node values of a neural network, the crossbar array comprising a plurality of row lines, a plurality of column lines, and a memory cell coupled between each combination of one row line and one column line. Also, an energy storing element disposed in the crossbar array between each combination of one row line and one column line and a filter that receives information from the energy storing element and provides new information for each node of the neural network.

Abstract: An oscillator circuit that includes a voltage source, a resistor, a capacitor, and a nonlinear device. The capacitor and the nonlinear device may be coupled in parallel with one another. The resistor may be coupled in series with the capacitor and the nonlinear device. The voltage source may be coupled in series with the resistor. The voltage source may supply the oscillator circuit with a direct current input signal. The nonlinear device may include an active layer coupled to a first electrode and a second electrode. In response to the direct current input signal, the oscillator circuit may output a spike train including a spike bunch.

Abstract: Systems are provided for implementing a hardware accelerator. The hardware accelerator emulate a stochastic neural network, and includes a first memristor crossbar array, and a second memristor crossbar array. The first memristor crossbar array can be programmed to calculate node values of the neural network. The nodes values can be calculated in accordance with rules to reduce an energy function associated with the neural network. The second memristor crossbar array is coupled to the first memristor crossbar array and programmed to introduce noise signals into the neural network. The noise signals can be introduced such that the energy function associated with the neural network converges towards a global minimum and modifies the calculated node values.

Abstract: A method for determining a solution to a constrained optimization problem includes programming a weights matrix of a Hopfield network with a first encoded matrix representation of an initial constrained optimization problem. The method also includes employing the Hopfield network to determine a solution to the initial constrained optimization problem. Additionally, the method includes encoding a plurality of constrained optimization problems associated with a target constrained optimization problem into a plurality of encoded matrix representations each of which are a combination of the first and the second encoded matrix representations. The plurality of encoded matrix representations increases in convergence to the second encoded matrix representation of the target constrained optimization problem sequentially. The method further includes re-programming the weights matrix of the Hopfield network in an iterative manner with the plurality of encoded matrix representations.

Abstract: A memristor device includes a first electrode, a second electrode, and a memristor layer disposed between the first electrode and the second electrode. The memristor layer is formed of a metal oxide. The memristor layer includes a plurality of regions that extend between the first electrode and the second electrode. The plurality of regions of the memristor layer are created with different concentrations of oxygen before electrical operation, and, during electrical operation, a voltage-conductance characteristic of the memristor device is controlled based on the different concentrations of oxygen of the plurality of regions. The controlling of the voltage-conductance characteristic includes increasing or decreasing the conductance of the memristor device toward a target conductance at a specific voltage.

Abstract: Examples herein relate to negative differential resistance devices. An example negative differential resistance device includes a first electrode and a first negative differential resistance device coupled to the first electrode. A second negative differential device is be coupled to the first negative differential resistance device. The second NDR device is different from the first NDR device. A second electrode is coupled to the second NDR device, and is electrically coupled with the first NDR device and the first electrode.

Abstract: In example implementations, a memory cell is provided. The memory cell includes a plurality of row lines and a plurality of column lines. The plurality of row lines and the plurality of column lines intersect to form a 2×2 array. The memory cell may include a plurality of memristors. A memristor is coupled to each unique combination of a row line and a column line in the 2×2 array. An input line is coupled to a first row of memristors. An invert is coupled to the input line. An inverted input line from the inverter is coupled to the second row of memristors.

Abstract: An input string is mapped to a vector of input voltages. The vector is applied to input rows of a dot product engine having memristor elements at intersections of the input rows and output columns. A hash of the input string is determined based on output of the dot product engine as to which the vector of input voltages have been applied to the input rows thereof. An output column may be selected from output voltages of the columns, and the hash determined from the selected column. The output voltage of a column is equal to a sum of a product of the input voltage in each input row and a value of the memristor element at the intersection of the input row and the column. The hash can be used within a filtering technique applied to the input string, such as in the context of network security.

Abstract: A crossbar array includes a number of memory elements. A vector input register has N voltage inputs to the crossbar array. A vector output register has M voltage outputs from the crossbar array. An analog-to-digital converter (ADC) is electronically coupled to the vector output register. A digital-to-analog converter (DAC) is electronically coupled to the vector input register. A clustering processor is electronically coupled to the ADC and to the DAC. The clustering processor is configured to program columns of the crossbar array with a set of k cluster center values; apply voltages to rows of the crossbar array where the applied voltages represent a set of data values; and determine a minimum distance of each data value to each k cluster center values based on the voltage output from the output register of each of the plurality of the programmed columns.

Abstract: A non-Boolean analog system includes a first Mott memristor having a first value of a characteristic, and a second Mott memristor having a second value of the characteristic different than the first value. The system includes a resistance in series with the first and second Mott memristors to form a network having a capacitance and that is operable as a relaxation oscillator. Responsive to electrical excitation, a temperature of the network operating an environment including ambient thermal noise settles at an equilibrium corresponding to a global minimum that is a maximally optimal global solution to a global optimization problem to which the network corresponds.

Abstract: In some examples, a device includes a nano-scale oscillator that exhibits chaotic oscillation responsive to a control input to the nano-scale oscillator, where the control input including a tunable input parameter.

Abstract: Examples herein relate to negative differential resistance devices. An example negative differential resistance device includes a first electrode and a first negative differential resistance device coupled to the first electrode. A second negative differential device is be coupled to the first negative differential resistance device. The second NDR device is different from the first NDR device. A second electrode is coupled to the second NDR device, and is electrically coupled with the first NDR device and the first electrode.

Abstract: An oscillator circuit that includes a voltage source, a resistor, a capacitor, and a nonlinear device. The capacitor and the nonlinear device may be coupled in parallel with one another. The resistor may be coupled in series with the capacitor and the nonlinear device. The voltage source may be coupled in series with the resistor. The voltage source may supply the oscillator circuit with a direct current input signal. The nonlinear device may include an active layer coupled to a first electrode and a second electrode. In response to the direct current input signal, the oscillator circuit may output a spike train including a spike bunch.

Abstract: In some examples, a device includes a nano-scale oscillator that exhibits chaotic oscillation responsive to a control input to the nano-scale oscillator, where the control input including a tunable input parameter.

Abstract: Techniques are disclosed relating to feedback-controlled oscillators (e.g., phase-locked loops) arranged in two or more levels. In some embodiments, in a relatively higher-frequency mode, a first level feedback-controlled oscillator provides reference signals to one or more second level feedback-controlled oscillators that in turn generate output clock signals to clock sequential circuitry. In some embodiments, in a relatively lower-frequency mode, the first level feedback-controlled oscillator bypasses the second level feedback-controlled oscillators and provides output clock signals directly to sequential circuitry (without using any intervening feedback-controlled oscillators).