Abstract: Embodiments of the present disclosure include systems and methods for determining position values for training data that is used to train transformer models. In some embodiments, a set of input data for training a transformer model is received. The set of input data comprises a set of tokens. Based on an offset value, a set of successive position values for the set of tokens is determined. Each position value in the set of successive position values represents a position of a token in the set of tokens relative to other tokens in the set of tokens. A set of training data is generated to comprise the set of tokens and the set of successive position values. The transformer model is trained using the set of training data.

Abstract: Embodiments of the present disclosure include systems and methods for packing tokens to train sequence models. In some embodiments, a plurality of datasets for training a sequence model is received. Each dataset in the plurality of datasets includes a sequence of correlated tokens. A set of training data is generated that includes a subset of a sequence of tokens from a first dataset in the plurality of datasets and a subset of a sequence of tokens from a second, different dataset in the plurality of datasets. The sequence model is trained using the set of training data.

Abstract: Embodiments of the present disclosure include systems and methods for training transformer models using position masking. In some embodiments, a set of data for training a transformer model is received. The set of data includes a sequence of tokens and a set of position values. Each position value in the set of position values represents a position of a token in the sequence of tokens relative to other tokens in the sequence of tokens. A subset of the set of position values in the set of data is selected. Each position value in the subset of the set of position values is replaced with a second defined value to form a second set of defined values. The transformer model is trained using the set of data.

Abstract: Embodiments of the present disclosure include systems and methods for reducing hardware resource utilization by residual neural networks. In some embodiments, a first matrix is received at a layer included in a neural network. The first matrix is compressed to produce a second matrix. The second matrix has a reduced dimensionality relative to a dimensionality of the first matrix. The second matrix is processed through a network block in the layer included in the neural network. The processed second matrix is expanded to produce a third matrix. The third matrix has a dimensionality that is equal to a dimensionality of the first matrix. The third matrix is added to the first matrix to produce a fourth matrix.

Abstract: The present techniques generally concern techniques for managing horticultural load shedding. A system includes a plurality of horticultural light sources powered by an electrical utility, the plurality of horticultural light sources being disposed in a horticultural structure to irradiate at least one plant or crop; a control module in data communication with the electrical utility, the control module being adapted to receive information associated with at least one load shedding event and, in response thereto, produce a control signal; and a rendering module adapted to receive and process the control signal; and send illumination instructions to the plurality of horticultural light sources to adjust an emission profile of the plurality of horticultural light, thereby mitigating potentially negative effects on said at least one plant or crop growth. A method is also provided.

Abstract: Systems and methods related to hardware-assisted gradient optimization using streamed gradients are described. An example method in a system comprising a memory configured to store weights associated with a neural network model comprising L layers, where L is an integer greater than one, a gradient optimizer, and a plurality of workers is described. The method includes during a single burst cycle moving a first set of gradients, received from each of the plurality of workers, from at least one gradient buffer to the gradient optimizer and moving weights from at least one buffer, coupled to the memory, to the gradient optimizer. The method further includes during the single burst cycle writing back the new weights, calculated by the gradient optimizer, to the memory. The method further includes during the single burst cycle transmitting the new weights, from the gradient optimizer, to each of the plurality of workers.

Abstract: Systems and methods related to dual-momentum gradient optimization with reduced memory requirements are described. An example method in a system comprising a gradient optimizer and a memory configured to store momentum values associated with a neural network model comprising L layers is described. The method includes retrieving from the memory a first set of momentum values and a second set of momentum values, corresponding to a layer of the neural network model, having a selected storage format. The method further includes converting the first set of momentum values to a third set of momentum values having a training format associated with the gradient optimizer and converting the second set of momentum values to a fourth set of momentum values having a training format associated with the gradient optimizer. The method further includes performing gradient optimization using the third set of momentum values and the fourth set of momentum values.

Abstract: A computer implemented method for managing horticultural lighting scenarios including the steps of receiving lighting scenarios and storing lighting scenario attributes thereof in a data storage; transmitting the lighting scenarios to a horticultural structure for deployment on at least one horticultural lighting apparatus; acquiring runtime data generated during the execution of the lighting scenarios and storing the runtime data on the data storage.

Abstract: Systems and methods related to hardware-assisted gradient optimization using streamed gradients are described. An example method in a system comprising a memory configured to store weights associated with a neural network model comprising L layers, where L is an integer greater than one, a gradient optimizer, and a plurality of workers is described. The method includes during a single burst cycle moving a first set of gradients, received from each of the plurality of workers, from at least one gradient buffer to the gradient optimizer and moving weights from at least one buffer, coupled to the memory, to the gradient optimizer. The method further includes during the single burst cycle writing back the new weights, calculated by the gradient optimizer, to the memory. The method further includes during the single burst cycle transmitting the new weights, from the gradient optimizer, to each of the plurality of workers.

Abstract: Embodiments of the present disclosure include an error recovery method comprising detecting a computing error, restarting a first artificial intelligence processor of a plurality of artificial intelligence processors processing a data set, and loading a model in the artificial intelligence processor, wherein the model corresponds to a same model processed by the plurality of artificial intelligence processors during a previous processing iteration by the plurality of artificial intelligence processors on data from the data set.

Abstract: Systems and methods for pipelined neural network processing with continuous and asynchronous updates are described. A method for processing a neural network comprising L layers, where L is an integer greater than two, includes partitioning the L layers among a set of computing resources configured to process forward passes and backward passes associated with each of the L layers. The method further includes initiating processing of the forward passes and the backward passes using the set of computing resources. The method further includes upon completion of a first set of forward passes and a first set of backward passes associated with a first layer of the L layers, initiating update of parameters associated with the first layer when gradients are available for updating the parameters associated with the first layer without waiting to calculate gradients associated with any of remaining L layers.

Abstract: Systems and methods related to dual-momentum gradient optimization with reduced memory requirements are described. An example method in a system comprising a gradient optimizer and a memory configured to store momentum values associated with a neural network model comprising L layers is described. The method includes retrieving from the memory a first set of momentum values and a second set of momentum values, corresponding to a layer of the neural network model, having a selected storage format. The method further includes converting the first set of momentum values to a third set of momentum values having a training format associated with the gradient optimizer and converting the second set of momentum values to a fourth set of momentum values having a training format associated with the gradient optimizer. The method further includes performing gradient optimization using the third set of momentum values and the fourth set of momentum values.

Abstract: Systems, methods, and apparatuses are provided for compressing values. A plurality of parameters may be obtained from a memory, each parameter comprising a floating-point number that is used in a relationship between artificial neurons or nodes in a model. A mantissa value and an exponent value may be extracted from each floating-point number to generate a set of mantissa values and a set of exponent values. The set of mantissa values may be compressed to generate a mantissa lookup table (LUT) and a plurality of mantissa LUT index values. The set of exponent values may be encoded to generate an exponent LUT and a plurality of exponent LUT index values. The mantissa LUT, mantissa LUT index values, exponent LUT, and exponent LUT index values may be provided to one or more processing entities to train the model.

Abstract: Embodiments of the present disclosure include systems and methods for training neural networks. In one embodiment, neural network may receive input data and produce output results in response to the input data and weights of the neural network. An error is determined at an output of the neural network based on the output results. The error is propagated in a reverse direction through the neural network from the output and one or more intermediate outputs to adjust the weights.

Abstract: A method of increasing computer hardware efficiency of a matrix computation. The method comprises receiving at a computer processing device, digital signals encoding one or more operations of the matrix computation, each operation including one or more operands. The method further comprises, responsive to determining, by a sparse data check device of the computer processing machine, that an operation of the matrix computation includes all dense operands, forwarding the operation to a dense computation device of the computer processing machine configured to perform the operation of the matrix computation based on the dense operands. The method further comprises, responsive to determining, by the sparse data check device, that an operation of the matrix computation includes one or more sparse operands, forwarding the operation to a sparse computation device configured to perform the operation of the matrix computation.

Abstract: The present techniques generally concern methods and systems for managing undesired effects in an electrical grid, which may include rapid voltage change(s) and/or flicker(s). The system includes an event detection module operatively connected to a plurality of horticultural light sources. The event detection module is configured to determine a power usage of the horticultural light sources, based on illumination conditions, detect an event affecting the illumination conditions, determine whether the event causes the undesired effects in the electrical grid, based on an evolution of the power usage of the horticultural light sources in response to the event, and send illumination instructions to the horticultural light sources to adjust the power usage of the horticultural light sources, if the event causes the undesired effects.

Abstract: The present techniques generally concern methods and systems for monitoring and managing reactive power from horticultural lighting sources in an electrical grid. The techniques provided herein include determining or predicting distortive effects produced by the horticultural lighting sources, evaluating a power factor of the horticultural light sources, and based on a target power factor, adjusting the power factor of the horticultural light sources. The techniques described herein allow for an optimization of the power factor of the horticultural lighting sources in order to reduce, mitigate or eliminate the negative effects generally associated with the operation of horticultural light sources on the electrical grid.

Abstract: Embodiments of the present disclosure include a method for token-position handling comprising: processing a first sequence of tokens to produce a second sequence of tokens, wherein the second sequence of tokens has a smaller number of tokens than the first sequence of tokens; masking at least some tokens in the second sequence to produce masked tokens; moving the masked tokens to the beginning of the second sequence to produce a third sequence; encoding tokens in the third sequence into a set of numeric vectors in a first array; and processing the first array in a transformer neural network to determine correlations among the third sequence, the processing the first array producing a second array.

Abstract: Embodiments of the present disclosure include systems and methods for compressing weights for distributed neural networks. In some embodiments, a first network comprising a first set of weights is trained using a set of training data. A second network comprising a second set of weights is trained using the set of training data. A number of weights in the first set of weights is greater than a number of weights in the second set of weights. The first set of weights are adjusted based on a first loss determined by the first network and a second loss determined by the second network. The second set of weights are adjusted based on the first loss determined by the first network and the second loss determined by the second network. Values of the second set of weights are sent to a computing system.

Abstract: Methods, systems, apparatuses, and computer program products are described herein that enable execution of a large AI model on a memory-constrained target device that is communicatively connected to a parameter server, which stores a master copy of the AI model. The AI model may be dissected into smaller portions (e.g., layers or sub-layers), and each portion may be executed as efficiently as possible on the target device. After execution of one portion of the AI model is finished, another portion of the AI model may be downloaded and executed at the target device. To improve efficiency, the input samples may be divided into microbatches, and a plurality of microbatches executing in sequential order may form a minibatch. The size of the group of microbatches or minibatch can be manually or automatically adjusted to reduce the communication overhead.