Winbond Electronics Corporation

Chun Lu

Bingxue Shi

Lu Chen

A current-mode pulse-width-modulation (PWM) circuit converts analog current signals into pulse signals. The PWM circuit includes a first I-V converter and one or more second I-V converters, each of the one or more second I-V converters being coupled to one of the current signals. Each of the first and second I-V converters is also coupled to a current generator which generates a current that linearly changes with time. For each of the first and second I-V converters, when a polarity of the input current thereof changes, an output changes between a high voltage level and a low voltage level. A logic circuit is coupled to the first and each second I-V converter to obtain a pulse signal that has a pulse width linearly proportional to the current level of the respective current signal.

High-precision current-mode pulse-width-modulation circuit

A current-mode synapse multiplier circuit multiplies each of a plurality of pulse signals with each of a corresponding plurality of weight signals. The synapse multiplier includes a plurality of first switches each coupled to a corresponding pulse signal and the corresponding weight signal. An integral circuit is coupled to the first switches to receive the weight signals that pass through the first switches and integrates the sum of the weight signals that pass through the first switches over a period of time. A voltage-to-current (V-I) converter is coupled to the integral circuit to convert the integral of the sum of the weight signals that pass through the first switches into a current signal, wherein the current signal represents the sum of the multiplication products of each pulse signal and the corresponding weight signal. An external reset signal is coupled to the synapse multiplier through a second switch to reset the synapse multiplier.

Current-mode synapse multiplier circuit

A neural network system includes a feedforward network comprising at least one neuron circuit for producing an activation function and a first derivative of the activation function and a weight updating circuit for producing updated weights to the feedforward network. The system also includes an error back-propagation network for receiving the first derivative of the activation function and to provide weight change data information to the weight updating circuit.

Back-propagation neural network with enhanced neuron characteristics

A neuron circuit generates a sigmoid transfer function and its derivative. The neuron circuit comprises an I-V converter that converts a current input signal into a voltage signal, a first output circuit having a first differential amplifier with a first current mirror as an active load, and a second output circuit having a second differential amplifier with a second current mirror as an active load, both the first and second output circuit being coupled to the voltage signal. While an output of one of the first and second output circuits is a sigmoid function of the current input, a difference between the two outputs of the two output circuits is a derivative of the sigmoid function.

Push-pull output neuron circuit

A voltage-mode pulse width modulation (PWM) VLSI implementation of neural networks, comprising: a voltage-pulse converter for converting an input voltage into a neuron-state pulse; a synapse multiplier, including a multiplier cell for multiplying the neuron-state pulse by an input weight voltage and an integral and summation cell for integrating and summing up the multiplied output and producing a first output voltage; and a sigmoid circuit for converting the first output voltage into a second output voltage with the non-linear activation function of neuron.

Voltage-mode pulse width modulation VLSI implementation of neural networks

Dual-switching and dual-linear power controller chip

A hybrid 4-in-1 power controller chip for a PC mainboard which integrates a synchronous buck switching regulator to supply the CPU core voltage, a standard buck switching regulator for I/O circuits, a linear controller for the GTL bus, and a linear regulator with built-in pass transistor for a clock generator. Therefore, a hybrid power system for a PC mainboard is constructed.

This invention relates to an artificial neural network (ANN), particularly to a neuron circuit and its activation function including the derivative. The neuron circuit capable of generating an adjustable sigmoid-like function and a good approximation of its derivative, comprises: a current generator for generating a current; a current-controlled transistor for changing an output voltage according to the current from the current generator; and at least one differential pair of transistors for generating the adjustable sigmoid-like function output and the good approximation of its derivative by the changed output voltage.

Generator of neuron transfer function and its derivative

A synchronous pulse width modulation (PWM) switching regulator that is suitable for high-performance CPUs. A DAC converts a plurality of inputted digital signal into analog signal that indicates the desired voltage. A circuit creates the necessary PWM signal to control a pair of drivers and their associated switches to convert a first inputted voltage to a second desired voltage. A soft-start circuit ensures that the startup voltage does not overshoot an expected level. Over-current and over-voltage protection circuits ensure that the circuit will not damage any circuits that power is supplied to.

Growing community of inventors

Beijing, China

Chun Lu