Abstract:
A method and apparatus for multi-level stepwise quantization for neural network are provided. The apparatus sets a reference level by selecting a value from among values of parameters of the neural network in a direction from a high value equal to or greater than a predetermined value to a lower value, and performs learning based on the reference level. The setting of a reference level and the performing of learning are iteratively performed until the result of the reference level learning satisfies a predetermined value and there is no variable parameter that is updated during learning among the parameters.
Abstract:
An analog-to-digital converter includes a capacitor array including capacitors, an amplifier receiving an input current through an input node and integrating the input current, using the capacitor array, in a first mode, a switch array including switches respectively connected to the capacitors, successive approximation logic performing a successive approximation by selectively connecting each of the capacitors to one of a common voltage and a reference voltage through the switches, in a second mode, a comparator comparing the common voltage with an output of the amplifier and output a pulse signal based on the comparison result, a counter counting the pulse signal in the first mode, a register sequentially storing values of the pulse signal, in the second mode, and error correction logic storing an output of the counter as upper bits and an output of the register as lower bits and performing error correction.
Abstract:
Provided is stretchable electronics. The stretchable electronics includes stretchable substrate, first support patterns disposed on a first surface of the stretchable substrate, and output devices disposed on the first patterns, respectively. The first support patterns are arranged in a first direction and a second direction, which are parallel to an extension direction of the substrate, and each of the output devices generates an output stimulation.
Abstract:
Disclosed is a neural network computing device. The neural network computing device includes a neural network accelerator including an analog MAC, a controller controlling the neural network accelerator in one of a first mode and a second mode, and a calibrator that calibrating a gain and a DC offset of the analog MAC. The calibrator includes a memory storing weight data, calibration weight data, and calibration input data, a gain and offset calculator reading the calibration weight data and the calibration input data from the memory, inputting the calibration weight data and the calibration input data to the analog MAC, receiving calibration output data from the analog MAC, and calculating the gain and the DC offset of the analog MAC, and an on-device quantizer reading the weight data, receiving the gain and the DC offset, generating quantized weight data, based on the gain and the DC offset.
Abstract:
Disclosed are a device and a method for calibrating a reference voltage. The reference voltage calibrating device includes a data signal communication unit that transmits/receives a data signal, a data strobe signal receiving unit that receives a first data strobe signal and a second data strobe signal, a voltage level of the second data strobe signal being opposite to a voltage level of the first data strobe signal, and a reference voltage generating unit that sets a reference voltage for determining a data value of the data signal, based on the first data strobe signal and the second data strobe signal, and the reference voltage generating unit adjusts the reference voltage based on the first data strobe signal and the second data strobe signal.
Abstract:
The neuromorphic arithmetic device comprises an input monitoring circuit that outputs a monitoring result by monitoring that first bits of at least one first digit of a plurality of feature data and a plurality of weight data are all zeros, a partial sum data generator that skips an arithmetic operation that generates a first partial sum data corresponding to the first bits of a plurality of partial sum data in response to the monitoring result while performing the arithmetic operation of generating the plurality of partial sum data, based on the plurality of feature data and the plurality of weight data, and a shift adder that generates the first partial sum data with a zero value and result data, based on second partial sum data except for the first partial sum data among the plurality of partial sum data and the first partial sum data generated with the zero value.
Abstract:
Disclosed is a transmitter which includes a channel driver that includes a pull-up transistor and a pull-down transistor connected between a power node and a ground node and outputs a voltage between the pull-up transistor and the pull-down transistor as a transmit signal, and a pre-driver that controls the pull-up transistor and the pull-down transistor in response to a driving signal and controls the channel driver such that the transmit signal is overshot at a rising edge of the driving signal and the transmit signal is undershot at a falling edge of the driving signal.
Abstract:
The neuromorphic arithmetic device performs a multiply-accumulate (MAC) calculation using a multiplier and an accumulator. The neuromorphic arithmetic device includes an offset accumulator configured to receive a plurality of offset data measured a plurality of times and accumulate the plurality of offset data, a bit extractor configured to obtain average offset data by extracting at least one first bit from the plurality of accumulated offset data, and a cumulative synapse array configured to accumulate a plurality of multiplication values generated by the multiplier and output a cumulative result of the plurality of multiplication values corrected according to the average offset data.
Abstract:
Provided is an analog-to-digital converting device. The analog-to-digital converting device may include a determination circuit that determination whether a reference digital signal or a determination digital signal obtained by conversion of a reference voltage or a determination voltage matches a test pattern for the reference voltage, and it is possible to monitor whether the analog-to-digital converting device normally operates, according to whether there is matching.
Abstract:
Provided is a sensing circuit for recognizing a movement including: at least one light emitting device outputting light; at least one light receiving device receiving the light reflected by an object on the light emitting device and generating a plurality of current signals proportional to an amount of incident light; a signal conversion unit converting the plurality of current signals into a plurality of digital signals; a recognition unit measuring a synthetic digital signal to determine whether an object moves by receiving the plurality of current signals; and a control unit controlling the recognition unit, wherein the recognition unit generates a clock signal for the synthetic digital signal greater than a critical value and measures a count generated by the clock signal; and the control unit determines whether the object moves through a comparison of the count and a reference value.