Abstract: Methods and systems are provided for operating a neuromorphic system for generating neuron and synapse activities. The method includes: preparing at least one digital timer in the neuromorphic system, each of the at least one digital timers including multi-bit digital values; generating time signals using the at least one digital timer; emulating an analog waveform of a neuron spike; updating parameters of the neuromorphic system using the time signals and the current values of the parameters; presetting, using a processor, the digital values of the at least one digital timer to initial values when the spike input is provided to the node; and updating, using the processor, the digital values of the at least one digital timer with a specified amount when there is an absence of a spike input to the node.

Abstract: An image processing apparatus is configured to generate a display image to be produced on a display system including a display unit. The image processing apparatus includes an acquisition unit configured to acquire orientation information indicating an orientation of an imaging apparatus when the imaging apparatus captures an input image, a setting unit configured to set a projection plane in a virtual space based on the orientation information, and a generation unit configured to generate the display image to be produced on the display unit with use of a relationship between the input image and the projection plane.

Abstract: A method and system are provided for updating synapse weight values in neuromorphic system with Spike Time Dependent Plasticity model. The method includes selectively performing, by a hardware-based synapse weight incrementer or decrementer, one of a synapse weight increment function or decrement function, each using a respective lookup table, to generate updated synapse weight values responsive to spike timing data. The method further includes storing the updated synapse weight values in a memory. The method additionally includes performing, by a hardware-based processor, a learning process to integrate the updated synapse weight values stored in the memory into the Spike Time Dependent Plasticity model neuromorphic system for improved neuromorphic simulation.

Abstract: A thermoelectric generator includes a perovskite dielectric substrate containing Sr and Ti and having electric conductivity by being doped to n-type; an energy filter formed on a top surface of the perovskite dielectric substrate, the energy filter including a first perovskite dielectric film, which contains Sr and Ti, has electric conductivity by being doped to n-type, and has a conduction band at an energy level higher than that of the perovskite dielectric substrate; a first electrode formed in electrical contact with a bottom surface of the perovskite dielectric substrate; and a second electrode formed in electrical contact with a top surface of the energy filter. The thermoelectric generator produces a voltage between the first and second electrodes by the top surface of the energy filter being exposed to a first temperature and the bottom surface of the perovskite dielectric substrate being exposed to a second temperature.

Abstract: An image processing apparatus includes a first acquisition unit configured to acquire an imaging angle of view at which an input image is captured, a second acquisition unit configured to acquire display system information indicating, in a display system including a display portion, a display angle, which is a visual angle at which the display portion is viewed from a viewpoint position, and a generation unit configured to, using a correspondence relationship between a projection plane and the display portion in a virtual space based on the imaging angle of view and the display system information, generate a display image to be displayed on the display portion.

Abstract: There is provided with an image-processing apparatus for indicating a range, which is displayed by a device or a system comprising a display area for displaying an image, within an input image. A first obtaining unit obtains information that represents a display form of the device or the system comprising the display area. A second obtaining unit obtains input image data representing the input image. An identification unit identifies the range, which is displayed in the display area, within the input image based on the input image data and the information. An output unit outputs information that represents the identified range. A shape of the identified range depends on the display area that corresponds to at least a curved screen or a plurality of flat screens.

Abstract: There is provided with an image processing apparatus that generates a display image to be displayed in a display system including a display area. An obtaining unit obtains one input image acquired through shooting by one image capturing apparatus. A generating unit generates the display image from the input image on the basis of a correspondence between a first projection plane corresponding to the input image and a second projection plane corresponding to the display area.

Abstract: A neuromorphic memory system including neuromorphic memory arrays. The neuromorphic memory system includes a presynaptic neuron circuit coupled to a postsynaptic neuron circuit by a resistive memory cell. The method includes generating a presynaptic LIF pulse on a presynaptic LIF line at time t1. An activating operation activates an access transistor coupled to the presynaptic LIF line in response to the presynaptic LIF pulse. The access transistor enables LIF current to pass through the resistive memory cell to a postsynaptic LIF line. An integrating operation integrates the LIF current at the postsynaptic LIF line over time. A comparing operation compares a LIF voltage at the postsynaptic LIF line to a threshold voltage. A generating operation generates a postsynaptic spike timing dependent plasticity (STDP) pulse on a postsynaptic STDP line if the LIF voltage is beyond the threshold voltage.

Abstract: Described is a neuromorphic system implemented in hardware that implements neuron membrane potential update based on the leaky integrate and fire (LIF) model. The system further models synapse weights update based on the spike time-dependent plasticity (STDP) model. The system includes an artificial neural network in which the update scheme of neuron membrane potential and synapse weight are effectively defined and implemented.

Abstract: A method and system are provided for updating a neuron membrane potential in a spike time dependent plasticity model in a Neuromorphic system. The method includes approximating a shape of an analog spike signal from an axon input using a hardware-based digital axon timer. The method further includes generating a first intermediately updated neuron membrane potential value from a current axon timer value, a current synapse weight value and a current neuron membrane potential value using a first look-up table and an accumulator. The method also includes generating a second intermediately updated neuron membrane potential value with a leak decay effect using a second look-up table and the first intermediately updated neuron membrane potential value. The method additionally includes generating a final updated neuron membrane potential value based on a comparison of the second intermediately updated neuron membrane potential value with a neuron fire threshold level using a comparator.

Abstract: A computer-implemented method is provided for refreshing cells in a Non-Volatile Memory (NVM)-based neuromorphic circuit wherein synapses are each composed of a respective cell pair formed from a respective Gp cell and a respective Gm cell of the cells. The method includes randomly selecting multiple neurons and reading a conductance of any of the synapses connected to the multiple neurons. The method further includes selecting any of the synapses connected to the selected multiple neurons for which the Gm conductance has reached a maximum conductance. The method also includes resetting the Gp cell and Gm cell of the selected synapses, and setting, at most, one of the Gp cell and Gm cell of each of the selected synapses to recover an effective total weight of each of the selected synapses.

Abstract: An information processing apparatus includes: a memory; and a processor coupled to the memory and configured to: obtain an actual measurement voltage at a time of a voltage change of a battery; estimate an internal impedance of the battery based on an equivalent circuit model of the battery in which a Warburg impedance is represented by an approximate equation using an equivalent circuit which is a series circuit of CR circuits and has steps of a power of 10 as a time constant of the CR circuit and the actual measurement voltage; determine a deterioration state of the battery based on the internal impedance; and notify of deterioration of the battery in a case where the deterioration diagnosis unit detects deterioration.

Abstract: A thermoelectric conversion element includes a film composed of a conductive oxide, a first electrode disposed on one end of the film composed of the conductive oxide, and a second electrode disposed on another end of the film composed of the conductive oxide, wherein the conductive oxide has a tetragonal crystal structure expressed by ABO3-x, where 0.1<x<1, wherein the conductive oxide has a band structure in which a Fermi level intersects seven bands between a ? point and an R point, and wherein the first electrode and the second electrode are disposed on the film composed of the conductive oxide so that electrical charge moves in a direction of a smallest vector among three primitive translation vectors of the crystal structure.

Abstract: A neuromorphic circuit, chip, and method are provided. The neuromorphic circuit includes a crossbar synaptic array cell. The crossbar synaptic array cell includes a Complimentary Metal-Oxide-Semiconductor (CMOS) transistor having an on-resistance controlled by a gate voltage of the CMOS transistor to update a weight of the crossbar synaptic array cell. The gate voltage of the CMOS transistor is controlled by performing a charge sharing technique that updates the weight of the crossbar synaptic array cell using non-overlapping pulses on control lines that are aligned with a set of row lines and a set of column lines.

Abstract: A tunable resistance device and methods of forming the same include a magnetic fixed layer having a fixed magnetization, a magnetic free layer, and a non-magnetic conductive layer directly between the magnetic fixed layer and the magnetic free layer. The magnetic fixed layer, the magnetic free layer, and the non-magnetic conductive layer are formed in a lattice of wires, with each wire in the lattice being formed from a stack of the magnetic fixed layer, the magnetic free layer, and the non-magnetic conductive layer.

Abstract: A neuromorphic circuit, chip, and method are provided. The neuromorphic circuit includes a crossbar synaptic array cell. The crossbar synaptic array cell includes a Complimentary Metal-Oxide-Semiconductor (CMOS) transistor having an on-resistance controlled by a gate voltage of the CMOS transistor to update a weight of the crossbar synaptic array cell. The gate voltage of the CMOS transistor is controlled by performing a charge sharing technique that updates the weight of the crossbar synaptic array cell using non-overlapping pulses on control lines that are aligned with a set of row lines and a set of column lines.

Abstract: A neuromorphic memory system including neuromorphic memory arrays. The neuromorphic memory system includes a presynaptic neuron circuit coupled to a postsynaptic neuron circuit by a resistive memory cell. The method includes generating a presynaptic LIF pulse on a presynaptic LIF line at time t1. An activating operation activates an access transistor coupled to the presynaptic LIF line in response to the presynaptic LIF pulse. The access transistor enables LIF current to pass through the resistive memory cell to a postsynaptic LIF line. An integrating operation integrates the LIF current at the postsynaptic LIF line over time. A comparing operation compares a LIF voltage at the postsynaptic LIF line to a threshold voltage. A generating operation generates a postsynaptic spike timing dependent plasticity (STDP) pulse on a postsynaptic STDP line if the LIF voltage is beyond the threshold voltage.

Abstract: A spiking neural network device includes: a spiking neural network circuit including a crossbar array of plural synapses; plural axons connected with the spiking neural network circuit, the plural axons receiving input of a spike signal; and plural Poisson spike generators respectively provided for the plural axons, each Poisson spike generator being configured to be set whether or not to emit the spike signal based on an input signal to be processed, each Poisson spike generator set to emit the spike signal being configured to generate a Poisson spike train different from each other and supply the Poisson spike train to a corresponding one of the plural axons.

Abstract: A neuromorphic memory system including neuromorphic memory arrays. The neuromorphic memory system includes a presynaptic neuron circuit coupled to a postsynaptic neuron circuit by a resistive memory cell. The method includes generating a presynaptic LIF pulse on a presynaptic LIF line at time t1. An activating operation activates an access transistor coupled to the presynaptic LIF line in response to the presynaptic LIF pulse. The access transistor enables LIF current to pass through the resistive memory cell to a postsynaptic LIF line. An integrating operation integrates the LIF current at the postsynaptic LIF line over time. A comparing operation compares a LIF voltage at the postsynaptic LIF line to a threshold voltage. A generating operation generates a postsynaptic spike timing dependent plasticity (STDP) pulse on a postsynaptic STDP line if the LIF voltage is beyond the threshold voltage.

Abstract: A method is provided of initializing a chip having synaptic NVRAM cells connected row-wise by word lines and column-wise by bit lines. The method includes selecting each word line through a row decoder connected to all word lines to switch all synaptic NVRAM cells of the selected lines. The method includes driving, on the selected lines, a wave generated by a PLL circuit connected to the row decoder. The method includes generating standing waves from the wave on the selected lines by implementing a resonance detection point at an input end of each word line. The method includes applying a write voltage on all bit lines through a column decoder connected to all bit lines. The method includes simultaneously driving each of the synaptic NVRAM cells of the selected lines by different writing currents for different durations in order to set different analog values to the synaptic NVRAM cells.