A convolutional neural network (CNN) method includes determining a temporary buffer layer, which is located between a first layer and a final layer of a CNN system; performing convolutional operations from the first layer to the determined temporary buffer layer of the CNN system in a first stage to generate a feature map line according to partial input data of layers before the temporary buffer layer; and performing convolutional operations from the temporary buffer layer to the final layer of the CNN system in a second stage to generate a feature map.

A convolutional neural network (CNN) method includes determining a temporary buffer layer, which is located between a first layer and a final layer of a CNN system; performing convolutional operations from the first layer to the determined temporary buffer layer of the CNN system in a first stage to generate a feature map line according to partial input data of layers before the temporary buffer layer; and performing convolutional operations from the temporary buffer layer to the final layer of the CNN system in a second stage to generate a feature map.

A method for verifying a calculation of a neuron value of multiple neurons of a neural network, including: carrying out or triggering a calculation of neuron functions of the multiple neurons, in each case to obtain a neuron value, the neuron functions being determined by individual weightings for each neuron input; calculating a first comparison value as the sum of the neuron values of the multiple neurons; carrying out or triggering a control calculation with one or multiple control neuron functions and with all neuron inputs of the multiple neurons, to obtain a second comparison value as a function of the neuron inputs of the multiple neurons and of the sum of the weightings of the multiple neurons assigned to the respective neuron input; and recognizing an error as a function of the first comparison value and of the second comparison value.

A method for verifying a calculation of a neuron value of multiple neurons of a neural network, including: carrying out or triggering a calculation of neuron functions of the multiple neurons, in each case to obtain a neuron value, the neuron functions being determined by individual weightings for each neuron input; calculating a first comparison value as the sum of the neuron values of the multiple neurons; carrying out or triggering a control calculation with one or multiple control neuron functions and with all neuron inputs of the multiple neurons, to obtain a second comparison value as a function of the neuron inputs of the multiple neurons and of the sum of the weightings of the multiple neurons assigned to the respective neuron input; and recognizing an error as a function of the first comparison value and of the second comparison value.

The application provides a processing method and device. Weights and input neurons are quantized respectively, and a weight dictionary, a weight codebook, a neuron dictionary, and a neuron codebook are determined. A computational codebook is determined according to the weight codebook and the neuron codebook. Meanwhile, according to the application, the computational codebook is determined according to two types of quantized data, and the two types of quantized data are combined, which facilitates data processing.

Aligning multiple 3D images of an object can be difficult when the representative datasets (images) are large. An exemplary aspect of this technology teaches a technique to subdivide the images and use the alignments between the subdivided images to determine the alignment between the complete datasets.

Aligning multiple 3D images of an object can be difficult when the representative datasets (images) are large. An exemplary aspect of this technology teaches a technique to subdivide the images and use the alignments between the subdivided images to determine the alignment between the complete datasets.

A depth positioning method to position a production logging tool (1) and a measurement log in a hydrocarbon well (3) in production obtained by means of the tool, the depth positioning method comprises: generating (S1, S2, S3, S1′, S2′, S3′, S11, S12, S13) a set of magnetic measurements (MAG1, MAG) of a depth portion of the hydrocarbon well from a first passive magnetic sensor along the depth portion of the hydrocarbon well, the set of magnetic measurements comprising magnitude and/or direction measurements of the magnetic field that forms a characteristic magnetic field pattern representative of a surrounding magnetic environment of the hydrocarbon well all along the depth portion; comparing (S4, S4′, S14) the set of magnetic measurements (MAG1, MAG) to another set of magnetic measurements (MAG_R, MAG2), the other set of magnetic measurements being a reference set of magnetic measurements generated either by a same or similar passive magnetic sensor deployed and run in the hydrocarbon well earlier, or by a second passive magnetic sensor spaced from the first passive magnetic sensor from a defined distance (DS) deployed and run in the hydrocarbon well simultaneously; and determining (S4, S4′, S14) the maximum of correlation between the set of magnetic measurements (MAG1, MAG) and the reference set of magnetic measurements (MAG_R, MAG2), the maximum being related to identifiable characteristic magnetic field pattern over a part of the depth portion.

A depth positioning method to position a production logging tool (1) and a measurement log in a hydrocarbon well (3) in production obtained by means of the tool, the depth positioning method comprises: generating (S1, S2, S3, S1′, S2′, S3′, S11, S12, S13) a set of magnetic measurements (MAG1, MAG) of a depth portion of the hydrocarbon well from a first passive magnetic sensor along the depth portion of the hydrocarbon well, the set of magnetic measurements comprising magnitude and/or direction measurements of the magnetic field that forms a characteristic magnetic field pattern representative of a surrounding magnetic environment of the hydrocarbon well all along the depth portion; comparing (S4, S4′, S14) the set of magnetic measurements (MAG1, MAG) to another set of magnetic measurements (MAG_R, MAG2), the other set of magnetic measurements being a reference set of magnetic measurements generated either by a same or similar passive magnetic sensor deployed and run in the hydrocarbon well earlier, or by a second passive magnetic sensor spaced from the first passive magnetic sensor from a defined distance (DS) deployed and run in the hydrocarbon well simultaneously; and determining (S4, S4′, S14) the maximum of correlation between the set of magnetic measurements (MAG1, MAG) and the reference set of magnetic measurements (MAG_R, MAG2), the maximum being related to identifiable characteristic magnetic field pattern over a part of the depth portion.

Described herein are systems and methods that increase the utilization and performance of computational resources, such as storage space and computation time, thereby, reducing computational cost. Various embodiments of the invention provide for a hardware structure that allows both streaming of source data that eliminates redundant data transfer and allows for in-memory computations that eliminate requirements for data transfer to and from intermediate storage. In certain embodiments, computational cost is reduced by using a hardware structure that enables mathematical operations, such as element-wise matrix multiplications employed by convolutional neural networks, to be performed automatically and efficiently.