Abstract: The performance of a neural network (NN) and/or deep neural network (DNN) can limited by the number of operations being performed as well as memory data management of a NN/DNN. Using vector quantization of neuron weight values, the processing of data by neurons can be optimize the number of operations as well as memory utilization to enhance the overall performance of a NN/DNN. Operatively, one or more contiguous segments of weight values can be converted into one or more vectors of arbitrary length and each of the one or more vectors can be assigned an index. The generated indexes can be stored in an exemplary vector quantization lookup table and retrieved by exemplary fast weight lookup hardware at run time on the fly as part of an exemplary data processing function of the NN as part of an inline de-quantization operation to obtain needed one or more neuron weight values.

Abstract: The performance of a neural network (NN) and/or deep neural network (DNN) can limited by the number of operations being performed as well as memory data management of a NN/DNN. Using vector quantization of neuron weight values, the processing of data by neurons can be optimize the number of operations as well as memory utilization to enhance the overall performance of a NN/DNN. Operatively, one or more contiguous segments of weight values can be converted into one or more vectors of arbitrary length and each of the one or more vectors can be assigned an index. The generated indexes can be stored in an exemplary vector quantization lookup table and retrieved by exemplary fast weight lookup hardware at run time on the fly as part of an exemplary data processing function of the NN as part of an inline de-quantization operation to obtain needed one or more neuron weight values.

Abstract: The performance of a neural network (NN) and/or deep neural network (DNN) can limited by the number of operations being performed as well as memory data management of a NN/DNN. Using vector quantization of neuron weight values, the processing of data by neurons can be optimize the number of operations as well as memory utilization to enhance the overall performance of a NN/DNN. Operatively, one or more contiguous segments of weight values can be converted into one or more vectors of arbitrary length and each of the one or more vectors can be assigned an index. The generated indexes can be stored in an exemplary vector quantization lookup table and retrieved by exemplary fast weight lookup hardware at run time on the flyas part of an exemplary data processing function of the NN as part of an inline de-quantization operation to obtain needed one or more neuron weight values.

Abstract: A color coding engine and a comparison engine are provided. A color coding engine may be utilized to detect logical layout object attributes in a flow format document and apply a unique color to textual elements associated with each logical layout object attribute. The resulting color coded document may be saved as a target flow format document. The target flow format document may be converted to a fixed format document and then converted by a conversion engine to a flow format document. The resulting converted flow format document may be saved as an output flow format document. A comparison engine may be utilized to compare the output flow format document and the target flow format document to determine if layout information has been properly preserved in the document conversion process.

Abstract: The performance of a neural network (NN) and/or deep neural network (DNN) can limited by the number of operations being performed as well as memory data management of a NN/DNN. Using vector quantization of neuron weight values, the processing of data by neurons can be optimize the number of operations as well as memory utilization to enhance the overall performance of a NN/DNN. Operatively, one or more contiguous segments of weight values can be converted into one or more vectors of arbitrary length and each of the one or more vectors can be assigned an index. The generated indexes can be stored in an exemplary vector quantization lookup table and retrieved by exemplary fast weight lookup hardware at run time on the flyas part of an exemplary data processing function of the NN as part of an inline de-quantization operation to obtain needed one or more neuron weight values.

Abstract: A color coding engine and a comparison engine are provided. A color coding engine may be utilized to detect logical layout object attributes in a flow format document and apply a unique color to textual elements associated with each logical layout object attribute. The resulting color coded document may be saved as a target flow format document. The target flow format document may be converted to a fixed format document and then converted by a conversion engine to a flow format document. The resulting converted flow format document may be saved as an output flow format document. A comparison engine may be utilized to compare the output flow format document and the target flow format document to determine if layout information has been properly preserved in the document conversion process.

Abstract: A fixed format document conversion engine and associated method for converting a fixed format document into a flow format document. The fixed format document conversion engine includes a sequence of layout analysis engines and semantic analysis engines to analyzes the base physical layout information obtained from the fixed format document to enrich, modify, and classify the physical layout information into progressively more advanced physical layout information and, ultimately, semantic layout information. The semantic layout information is mapped and serialized into a selected flow format document with a high level of flowability.