According to one embodiment, a method, computer system, and computer program product for increasing linearity of a weight update of a phase change memory (PCM) cell is provided. The present invention may include applying a RESET pulse to amorphize the phase change material of the PCM cell; responsive to applying the RESET pulse, applying an incubation pulse to the PCM cell; and applying a plurality of partial SET pulses to incrementally increase the conductance of the PCM cell.

A method and apparatus for systems and methods for digital signal processing (DSP) in a non-volatile memory (NVM) device comprising CMOS coupled to NVM die, of a data storage device. According to certain embodiments, one or more DSP calculations are provided by a controller to the CMOS components of the NVM, that configure one or more memory die to carry out atomic calculations on the data resident on the die. The results of calculations of each die are provided to an output latch for each die, back-propagating data back to the configured calculation portion as needed, otherwise forwarding the results to the controller. The controller aggregates the results of DSP calculations of each die and presents the results to the host system.

A device for determining the amount or concentration of an analyte in a fluid sample and a flow rate of the fluid sample in a channel is provided. The device includes a chamber including a channel and an opening the channel in fluid communication with the opening. The device further includes a wicking component positioned adjacent to the opening configured to receive an amount of fluid from the channel. The device may further include an analyte sensor positioned on the wicking component, the analyte sensor configured to detect an analyte in fluid in contact with the analyte sensor, wherein the wicking component is configured to contact the amount of fluid with the analyte sensor. Alternatively the device may include at least one pair of electrodes configured to determine a flow rate of the fluid in the channel.

In order to function reliably, a classical computer suppresses quantum uncertainty while a quantum computer harnesses uncertainty to provide additional computational resource. Both classical and quantum computers operate in a background dependent deterministic framework and process information in a step-by-step fashion. A quantum gravity computer, on the other hand, has indefinite causal structure caused by the interplay between general relativity and quantum mechanics and cannot be modeled as a step-by-step process. It does not ‘compute’ in the traditional sense but still processes information according to rules. Such a computer has greater power than a step computer and should have application to simulating systems where both quantum mechanics and general relativity re important, such as the early stages of our Universe. It may also serve as the model for the operation of the human brain, giving rise to such faculties as understanding, free will, and creativity.

A method and apparatus with artificial network generation and/or implementation corresponding to a natural neural network are disclosed. The method includes performing an adjusting, based on a firing time difference between an action potential (AP) of a first biological neuron and a post-synaptic potential (PSP) of a second biological neuron, of a first conductance value of a first memory corresponding to the first and second biological neurons, adjusting the firing time difference based on the PSP of the second biological neuron, and performing another adjusting, by controlling another firing time difference between the AP of the first biological neuron and the PSP of the second biological neuron to be the adjusted firing time difference, of a second conductance value of the first memory corresponding to the first and second biological neurons.

A diffusive memristor device and an electronic device for emulating a biological neuron is disclosed. The diffusive memristor device includes a bottom electrode, a top electrode formed opposite the bottom electrode, and a dielectric layer disposed between the top electrode and the bottom electrode. The dielectric layer comprises an oxide doped with a metal.

A method includes: generating a first sum value at least by a first resistor; generating a first shifted sum value based on the first sum value and a nonlinear function; generating a pulse number based on the first shifted sum value; and changing the first resistor based on the pulse number to adjust the first sum value.

A device includes first wires, second wires, resistors, and a processor. Input signals are transmitted from the first wires through the resistors to the second wires. The processor receives a sum value of the input signals from one of the second wires, and shifts the sum value by a nonlinear activation function to generate a shifted sum value. The processor calculates a backpropagation value based on the shifted sum value and a target value, and generates a pulse number based on a corresponding input signal of the input signal and the backpropagation value. Each of a value of the corresponding input signal and the backpropagation value is higher than or equal to a threshold value. The processor applies a voltage pulse to one of the resistors related to the corresponding input signal based on the pulse number.

A generative adversarial network device and a training method thereof. The generative adversarial network device includes a generator and a discriminator. The generator is configured to generate a first sample according to an input data; the discriminator is coupled to the generator, and is configured to receive the first sample and be trained based on the first sample; the generator includes a first memristor array serving as a first weight array. The generative adversarial network device can omit a process of adding noise to fake samples generated by the generator, thereby saving training time, reducing resource consumption and improving training speed of the generative adversarial network.

An interface structure for a biological environment including at least one composite electrical impulse generating layer comprising a matrix phase of a piezo polymer material, a first dispersed phase of piezo nanocrystals, and second dispersed phase of carbon nanotubes, the first and second dispersed phase presented through the matrix phase. The piezo polymer material and piezo nanocrystal convert mechanical motion into electrical impulses and accept electrons to charge the composite impulse generating layer. The carbon nanotubes provide pathways for distribution of the electrical impulses to a surface of the composite impulse generating layer contacting the biological environment. The carbon nanotubes further provide for the delivery of the byproducts of the free radical degradation from the biological environment to both piezo-nanocrystals and piezo-polymer.