Abstract: Hardware implementations of Deep Neural Networks (DNNs) and related methods with a variable output data format. Specifically, in the hardware implementations and methods described herein the hardware implementation is configured to perform one or more hardware passes to implement a DNN wherein during each hardware pass the hardware implementation receives input data for a particular layer, processes that input data in accordance with the particular layer (and optionally one or more subsequent layers), and outputs the processed data in a desired format based on the layer, or layers, that are processed in the particular hardware pass. In particular, when a hardware implementation receives input data to be processed, the hardware implementation also receives information indicating the desired format for the output data of the hardware pass and the hardware implementation is configured to, prior to outputting the processed data convert the output data to the desired format.

Abstract: A hardware downscaling module and downscaling methods for downscaling a two-dimensional array of values. The hardware downscaling unit comprises a first group of one-dimensional downscalers; and a second group of one-dimensional downscalers; wherein the first group of one-dimensional downscalers is arranged to receive a two-dimensional array of values and to perform downscaling in series in a first dimension; and wherein the second group of one-dimensional downscalers is arranged to receive an output from the first group of one-dimensional downscalers and to perform downscaling in series in a second dimension.

Abstract: Hardware implementations of DNNs and related methods with a variable output data format. Specifically, in the hardware implementations and methods described herein the hardware implementation is configured to perform one or more hardware passes to implement a DNN wherein during each hardware pass the hardware implementation receives input data for a particular layer, processes that input data in accordance with the particular layer (and optionally one or more subsequent layers), and outputs the processed data in a desired format based on the layer, or layers, that are processed in the particular hardware pass. In particular, when a hardware implementation receives input data to be processed, the hardware implementation also receives information indicating the desired format for the output data of the hardware pass and the hardware implementation is configured to, prior to outputting the processed data convert the output data to the desired format.

Abstract: Methods and systems for implementing a traditional computer vision algorithm as a neural network. The method includes: receiving a definition of the traditional computer vision algorithm that identifies a sequence of one or more traditional computer vision algorithm operations; mapping each of the one or more traditional computer vision algorithm operations to a set of one or more neural network primitives that is mathematically equivalent to that traditional computer vision algorithm operation; linking the one or more network primitives mapped to each traditional computer vision algorithm operation according to the sequence to form a neural network representing the traditional computer vision algorithm; and configuring hardware logic capable of implementing a neural network to implement the neural network that represents the traditional computer vision algorithm.

Abstract: Methods and systems for processing data in accordance with a neural network that includes a sequence of layers comprising a first convolution layer, a second convolution layer, and none, one or more than one middle layer between the first and second convolution layers. The method includes: scaling, using hardware logic, a tensor in the neural network, after the first convolution layer and before the second convolution layer, on a per channel basis by a set of per channel activation scaling factors; and implementing, using the hardware logic, the second convolution layer with weights that have been scaled on a per input channel basis by the inverses of the set of per channel activation scaling factors.

Abstract: The present invention provides methods for treating depression in a patient, comprising administering to the patient in need of the treatment a therapeutically effective amount of esketamine. In some embodiments, the depression is major depressive disorder or treatment resistant depression. In other embodiments, the therapeutically effective amount is clinically proven safe and/or effective. Also provided are methods to mitigate the risk or misuse or abuse of esketamine, instructions for use of the esketamine product, and methods for selling a drug product containing esketamine.

Abstract: Hardware implementations of, and methods for processing, a convolution layer of a DNN that comprise a plurality of convolution engines wherein the input data and weights are provided to the convolution engines in an order that allows input data and weights read from memory to be used in at least two filter-window calculations performed either by the same convolution engine in successive cycles or by different convolution engines in the same cycle. For example, in some hardware implementations of a convolution layer the convolution engines are configured to process the same weights but different input data each cycle, but the input data for each convolution engine remains the same for at least two cycles so that the convolution engines use the same input data in at least two consecutive cycles.

Abstract: A downhole closed loop method for controlling a curvature of a subterranean wellbore while drilling includes controlling a direction of drilling such that the drilling attitude is substantially equal to a setpoint attitude. A setpoint rate of penetration is processed in combination with a setpoint dogleg severity to compute a setpoint attitude increment. The setpoint attitude may be adjusted by the setpoint attitude increment. The setpoint attitude may be incremented at some interval to control the curvature of the wellbore while drilling.

Abstract: A neural network accelerator includes a plurality of hardware processing units, each hardware processing unit comprising hardware to accelerate performing one or more neural network operations on data; and a crossbar coupled to each hardware processing unit of the plurality of hardware processing units and configured to selectively form, from a plurality of selectable pipelines, a pipeline from one or more of the hardware processing units of the plurality of hardware processing units to process input data to the neural network accelerator. The plurality of hardware processing units comprising (i) a convolution processing unit configured to accelerate performing convolution operations on data, and (ii) a configurable pooling processing unit configured to selectively perform an operation of a plurality of selectable operations on data, the plurality of selectable operations comprising a depth-wise convolution operation and one or more pooling operations.

Abstract: A hardware implementation of a configurable pooling processing unit is configured to receive an input tensor comprising at least one channel, each channel of the at least one channel comprising a plurality of tensels; receive control information identifying one operation of a plurality of selectable operations to be performed on the input tensor, the plurality of selectable operations comprising a depth-wise convolution operation and one or more pooling operations; perform the identified operation on the input tensor to generate an output tensor by performing one or more operations on blocks of tensels of each channel of the at least one channel of the input tensor; and output the output tensor.

Abstract: A downhole closed loop method for controlling a curvature of a subterranean wellbore while drilling includes controlling a direction of drilling such that the drilling attitude is substantially equal to a setpoint attitude. A setpoint rate of penetration is processed in combination with a setpoint dogleg severity to compute a setpoint attitude increment. The setpoint attitude may be adjusted by the setpoint attitude increment. The setpoint attitude may be incremented at some interval to control the curvature of the wellbore while drilling.

Abstract: Methods and systems for implementing a traditional computer vision algorithm as a neural network. The method includes: receiving a definition of the traditional computer vision algorithm that identifies a sequence of one or more traditional computer vision algorithm operations; mapping each of the one or more traditional computer vision algorithm operations to a set of one or more neural network primitives that is mathematically equivalent to that traditional computer vision algorithm operation; linking the one or more network primitives mapped to each traditional computer vision algorithm operation according to the sequence to form a neural network representing the traditional computer vision algorithm; and configuring hardware logic capable of implementing a neural network to implement the neural network that represents the traditional computer vision algorithm.

Abstract: A downhole closed loop method for controlling a curvature of a subterranean wellbore while drilling includes controlling a direction of drilling such that the drilling attitude is substantially equal to a setpoint attitude. A setpoint rate of penetration is processed in combination with a setpoint dogleg severity to compute a setpoint attitude increment. The setpoint attitude may be adjusted by the setpoint attitude increment. The setpoint attitude may be incremented at some interval to control the curvature of the wellbore while drilling.

Abstract: A hardware downscaler and an architecture for implementing a FIR filter in which the downscaler can be arranged for downscaling by a half in one dimension. The downscaler can comprise: hardware logic implementing a first three-tap FIR filter; and hardware logic implementing a second three-tap FIR filter; wherein the output from the hardware logic implementing the first three-tap filter is provided as an input to the hardware logic implementing the second three-tap filter.

Abstract: A hardware downscaling module and downscaling methods for downscaling a two-dimensional array of values. The hardware downscaling unit comprises a first group of one-dimensional downscalers; and a second group of one-dimensional downscalers; wherein the first group of one-dimensional downscalers is arranged to receive a two-dimensional array of values and to perform downscaling in series in a first dimension; and wherein the second group of one-dimensional downscalers is arranged to receive an output from the first group of one-dimensional downscalers and to perform downscaling in series in a second dimension.

Abstract: Hardware implementations of, and methods for processing, a convolution layer of a DNN that comprise a plurality of convolution engines wherein the input data and weights are provided to the convolution engines in an order that allows input data and weights read from memory to be used in at least two filter-window calculations performed either by the same convolution engine in successive cycles or by different convolution engines in the same cycle. For example, in some hardware implementations of a convolution layer the convolution engines are configured to process the same weights but different input data each cycle, but the input data for each convolution engine remains the same for at least two cycles so that the convolution engines use the same input data in at least two consecutive cycles.

Abstract: Hardware implementations of, and methods for processing, a convolution layer of a DNN that comprise a plurality of convolution engines wherein the input data and weights are provided to the convolution engines in an order that allows input data and weights read from memory to be used in at least two filter-window calculations performed either by the same convolution engine in successive cycles or by different convolution engines in the same cycle. For example, in some hardware implementations of a convolution layer the convolution engines are configured to process the same weights but different input data each cycle, but the input data for each convolution engine remains the same for at least two cycles so that the convolution engines use the same input data in at least two consecutive cycles.

Abstract: A downhole closed loop method for controlling a curvature of a subterranean wellbore while drilling includes controlling a direction of drilling such that the drilling attitude is substantially equal to a setpoint attitude. A setpoint rate of penetration is processed in combination with a setpoint dogleg severity to compute a setpoint attitude increment. The setpoint attitude may be adjusted by the setpoint attitude increment. The setpoint attitude may be incremented at some interval to control the curvature of the wellbore while drilling.

Abstract: Hardware implementations of, and methods for processing, a convolution layer of a DNN that comprise a plurality of convolution engines wherein the input data and weights are provided to the convolution engines in an order that allows input data and weights read from memory to be used in at least two filter-window calculations performed either by the same convolution engine in successive cycles or by different convolution engines in the same cycle. For example, in some hardware implementations of a convolution layer the convolution engines are configured to process the same weights but different input data each cycle, but the input data for each convolution engine remains the same for at least two cycles so that the convolution engines use the same input data in at least two consecutive cycles.

Abstract: A downhole closed loop method for controlling a curvature of a subterranean wellbore while drilling includes controlling a direction of drilling such that the drilling attitude is substantially equal to a setpoint attitude. A setpoint rate of penetration is processed in combination with a setpoint dogleg severity to compute a setpoint attitude increment. The setpoint attitude may be adjusted by the setpoint attitude increment. The setpoint attitude may be incremented at some interval to control the curvature of the wellbore while drilling.