Provided are a method for constructing a Morton code, an encoder, decoder and computer storage medium. The encoder determines geometric information of a current point in a point cloud; determines an index value corresponding to a coordinate component according to the coordinate component in the geometric information; determines a Morton code component corresponding to the coordinate component according to the index value and a Morton code lookup table; determines a Morton code of the current point according to the Morton code component. The encoder analyzes a bitstream to determine geometric information of a current point in a point cloud; determines an index value corresponding to a coordinate component according to the coordinate component in geometric information; determines a Morton code component corresponding to the coordinate component according to the index value and a Morton code lookup table; determines a Morton code of the current point according to Morton code component.

A processor-implemented data processing method includes encoding a plurality of weights of a filter of a neural network using an inverted two's complement fixed-point format; generating weight data based on values of the encoded weights corresponding to same filter positions of a plurality of filters; and performing an operation on the weight data and input activation data using a bit-serial scheme to control when to perform an activation function with respect to the weight data and input activation data.

A processor-implemented data processing method includes encoding a plurality of weights of a filter of a neural network using an inverted two's complement fixed-point format; generating weight data based on values of the encoded weights corresponding to same filter positions of a plurality of filters; and performing an operation on the weight data and input activation data using a bit-serial scheme to control when to perform an activation function with respect to the weight data and input activation data.

A semiconductor device according to one embodiment executes a neural network processing. A first shift register sequentially generates a plurality of pieces of quantized input data by quantizing a plurality of pieces of output data sequentially inputted from a first buffer by bit-shifting. A product-sum operator generates operation data by performing a product-sum operation to a plurality of parameters and the plurality of pieces of quantized input data from the first shift register. The second shift register generates the output data by inversely quantizing the operation data from the product-sum operator by bit-shifting, and stores the output data in the first buffer.

A semiconductor device according to one embodiment executes a neural network processing. A first shift register sequentially generates a plurality of pieces of quantized input data by quantizing a plurality of pieces of output data sequentially inputted from a first buffer by bit-shifting. A product-sum operator generates operation data by performing a product-sum operation to a plurality of parameters and the plurality of pieces of quantized input data from the first shift register. The second shift register generates the output data by inversely quantizing the operation data from the product-sum operator by bit-shifting, and stores the output data in the first buffer.

A processor-implemented artificial neural network quantization scheme implementation method and apparatus are provided. The method includes receiving input data corresponding to a first M-dimensional vector, receiving a weight parameter corresponding to a second M-dimensional vector, encoding the input data into first bit streams, each having “N” layers, with a predetermined quantization scheme, encoding the weight parameter into second bit streams, each having “N” layers, with the quantization scheme, applying corresponding first and second bit streams to a binary neural network operator, for each of possible combinations between layers of the first bit streams and layers of the second bit streams, receiving a dot product result output based on a result obtained by shifting a BNN operation result corresponding to each of the combinations by a number of corresponding bits and accumulating the shifted BNN operation result, from the BNN operator, and quantizing the dot product result using the quantization scheme.

A processor-implemented artificial neural network quantization scheme implementation method and apparatus are provided. The method includes receiving input data corresponding to a first M-dimensional vector, receiving a weight parameter corresponding to a second M-dimensional vector, encoding the input data into first bit streams, each having “N” layers, with a predetermined quantization scheme, encoding the weight parameter into second bit streams, each having “N” layers, with the quantization scheme, applying corresponding first and second bit streams to a binary neural network operator, for each of possible combinations between layers of the first bit streams and layers of the second bit streams, receiving a dot product result output based on a result obtained by shifting a BNN operation result corresponding to each of the combinations by a number of corresponding bits and accumulating the shifted BNN operation result, from the BNN operator, and quantizing the dot product result using the quantization scheme.

Aspects of the present disclosure address systems and methods for fixed-point quantization using a dynamic quantization level adjustment scheme. Consistent with some embodiments, a method comprises accessing a neural network comprising floating-point representations of filter weights corresponding to one or more convolution layers. The method further includes determining a peak value of interest from the filter weights and determining a quantization level for the filter weights based on a number of bits in a quantization scheme. The method further includes dynamically adjusting the quantization level based on one or more constraints. The method further includes determining a quantization scale of the filter weights based on the peak value of interest and the adjusted quantization level. The method further includes quantizing the floating-point representations of the filter weights using the quantization scale to generate fixed-point representations of the filter weights.

A technique for bit depth up-conversion including obtaining an input value for a computation in a first bit depth with a fewer number of bits as compared to a second bit depth, converting the input value from the first bit depth to the second bit depth as an unsigned data value, adjusting a pointer to the converted input value based on the first bit depth, performing the computation based on the adjusted pointer to obtain an adjusted output value, and performing a right shift operation on the adjusted output value based on the first bit depth to obtain an output value.

A method for processing data sets having at least one time series. These are measurement values of sensors that are sensed at certain times. The data of the measured values and the respective times at which the data were sensed are stored as data elements of the time series. The method compresses the data set by rounding the sensed data with subsequent decimation of the data elements of the data set which are contained in the time series.