7results about "Computing operation arrangements" patented technology

Systems, methods, and devices for predictive maintenance of machinery are provided. An exemplary apparatus includes a vibration sensor configured to sense vibrations of a vibration source and an analog circuit. The analog circuit includes a plurality of operational amplifiers and a plurality of resistors. The analog circuit is coupled to the vibration sensor and configured to receive an analog signal from the vibration sensor and to calculate an output based on the analog signal by executing a portion of a trained neural network.

Systems, methods, and devices are provided for predictive maintenance of machines. An example apparatus includes a vibration sensor configured to sense vibrations of a vibration source and an analog circuit. The analog circuit comprises a plurality of operational amplifiers and a plurality of resistors. The analog circuit is coupled to the vibration sensor and configured to: receive an analog signal from the vibration sensor; and compute an output based on the analog signal, by performing a portion of a trained neural network.

The utility model provides the differential sampling calculation circuit formed by the operational amplifier, the divider resistor of the differential sampling calculation circuit adopts a common high-precision resistor to replace an ultra-high-precision divider resistor, and the divided output voltage is not directly connected with the sampling signal error amplifier, so that more stable and efficient output voltage sampling can be realized. The circuit comprises a sampling signal error amplifier IC1, and a differential sampling circuit is arranged between the sampling signal error amplifier IC1 and a positive end VO + and a negative end VO-of output voltage, so that the output voltage is prevented from being directly connected with the sampling signal error amplifier in a partial voltage manner, efficient output voltage sampling can be realized, and the safety of the whole circuit is guaranteed.

A method and an apparatus for processing an analog input signal and generating a corresponding analog output signal are disclosed. The input signal is supplied to an analog delay line comprising analog memory cells. A propagation signal causes the input signal to propagate through the sequence of memory cells in discrete time steps. Each memory cell is buffered and weighted by the stored analog parameters, and the sum of weighted memory cell values is accumulated as an analog signal. The result of accumulation is passed through an activation function. Multiple such blocks provide a convolutional neural network that works at extremely low power in comparison with its digital equivalent.

The present application provides a true random number generator and a random number generation method based on a magnetic tunnel junction device. The true random number generator provided by the present application includes a control circuit, a magnetic tunnel junction device, and a random number generator; the control circuit is used to provide multiple continuous voltage pulse signals to the magnetic tunnel junction device, causing the resistance of the magnetic tunnel junction device to oscillate; the multiple voltage pulse signals correspond to different flipping probabilities of the magnetic tunnel junction device, and after the multiple continuous voltage pulse signals act synergistically on the magnetic tunnel junction device, the magnetic tunnel junction device accumulates the flipping probabilities based on in-memory calculations so that the final flipping probability after accumulation tends to be half-and-half probability; the random number generator is used to generate random numbers based on the resistance state of the magnetic tunnel junction device after the multiple continuous voltage pulse signals are applied. The true random number generator and random number generation method based on the magnetic tunnel junction device provided by the present application can generate random numbers with high randomness and uniform distribution.

There is provided a circuit comprising a mirrored enhanced Howland current source, the mirrored enhanced Howland current source comprising an input and an output; and a feedback loop, the feedback loop comprising: an instrumentation amplifier arranged to measure a current at the output of the mirrored enhanced Howland current source; a comparator arranged to compare an output of the instrumentation amplifier to a reference voltage source; an integrator electrically coupled to the output of the comparator; a multiplier arranged to multiply the output of the integrator with the reference voltage, wherein the output of the multiplier is electrically coupled to the input of the mirrored enhanced Howland current source.

A method and an apparatus for processing an analog input signal using an analog neural network and generating a corresponding analog output signal are disclosed. An analog input interface is configured to supply a task input signal and a compensation input signal. One or more analog neuron circuits are connected to the analog input interface. An analog parameter storage array modifies task input signals and supplies these to an analog computation circuit that generates an output signal based on a combination of contributions from the modified task input signals and the compensation input signal.