Abstract: Disclosed herein is a graph streaming processing system comprising a thread scheduler comprising a first component and a second component. The first component is configured to schedule a first set of threads of a first node to a first processor associated with the first node and initialize status of a completion pointer to an initial value. The completion pointer is associated with a command buffer of the first node. The first component is configured to detect the execution of the first set of threads and generation of a data unit and update the status of the completion pointer to an updated value indicating execution of the first set of threads in response to the generation of the data unit. The second component is configured to schedule a second set of threads of a plurality of second nodes to a second processor based on the status of the completion pointer.

Abstract: Methods, systems, and apparatuses for adaptive power supply voltage transient protection are disclosed. One system includes a power supply, a transient sensor, and a power control processing entity. The power supply operates to provide power to one or more processors. The transient sensor is connected to the power supply and operates to sense transients on the power supply at greater than a predetermined speed or rate. The power control processing entity operates to receive a representation of the sensed transients and adjust a power load based on the sensed transients.

Abstract: Methods, systems, and apparatuses for unsupervised data drift detection for classification neural networks are disclosed. One method includes providing the data stream of images to a neural network, generating, by the neural network, class wise probabilities, storing each image of the data stream of images, storing the class wise probabilities generated by the neural network, comparing artifacts of images of the data stream at a first time with artifacts of images of the data stream at a second time, comparing artifacts produced by the class wise probabilities of the data stream retrieved from the stored class wise probabilities at a third time with artifacts produced by the class wise probabilities of the data stream retrieved from the stored class wise probabilities at a fourth time, and generating an informative communication based on the comparisons.

Abstract: Methods, systems and apparatuses for discovering novel artificial neural network architectures (ANN) architecture are disclosed.

Abstract: Methods, systems, and apparatuses for adaptive power supply voltage transient protection are disclosed. One system includes a power supply, a voltage transient sensor, and a power control processing entity. The power supply operates to provide power to one or more processors. The voltage transient sensor is connected to the power supply and operates to sense voltage transients on the power supply at greater than a predetermined speed or rate. The power control processing entity operates to receive a representation of the sensed voltage transients and adjust a power load based on the sensed voltage transients.

Abstract: Systems and methods are disclosed for predicting one or more medical conditions utilizing digital images and employing artificial intelligent algorithms. The system offers accurate predictions utilizing quantized pre-trained deep learning model. The pre-trained deep learning model is trained on data samples and later refined as the system processes more digital images or new medical conditions are incorporated. One pre-trained deep learning model is used to predict the probability of one or more medical conditions and identify locations in the digital image effected by the one or more medical conditions. Further, one pre-trained deep learning model utilizing additional data and plurality of digital images, forecasts rate of infection and spread of the medical condition over time.

Abstract: Disclosed herein is a graph streaming processing system comprising a thread scheduler comprising a first component and a second component. The first component is configured to schedule a first set of threads of a first node to a first processor associated with the first node and initialize status of a completion pointer to an initial value. The completion pointer is associated with a command buffer of the first node. The first component is configured to detect the execution of the first set of threads and generation of a data unit and update the status of the completion pointer to an updated value indicating execution of the first set of threads in response to the generation of the data unit. The second component is configured to schedule a second set of threads of a plurality of second nodes to a second processor based on the status of the completion pointer.

Abstract: The present disclosure relates to a system and method of performing quantization of a neural network having multiple layers. The method comprises receiving a floating-point dataset as input dataset and determining a first shift constant for first layer of the neural network based on the input dataset. The method also comprises performing quantization for the first layer using the determined shift constant of the first layer. The method further comprises determining a next shift constant for next layer of the neural network based on output of a layer previous to the next layer, and performing quantization for the next layer using the determined next shift constant. The method further comprises iterating the steps of determining shift constant and performing quantization for all layers of the neural network to generate fixed point dataset as output.

Abstract: Methods, systems, and apparatuses for graph stream processing are disclosed. One apparatus includes a cascade of graph streaming processors, wherein each of the graph streaming processor includes a processor array, and a graph streaming processor scheduler. The cascade of graph streaming processors further includes a plurality of shared command buffers, wherein each shared command buffer includes a buffer address, a write pointer, and a read pointer, wherein for each of the plurality of shared command buffers a graph streaming processor writes commands to the shared command buffer as indicated by the write pointer of the shared command buffer and the graph streaming processor reads commands from the shared command buffer as indicated by the read pointer, wherein at least one graph streaming processor scheduler operates to manage the write pointer and the read pointer to avoid overwriting unused commands of the shared command buffer.

Abstract: Methods, systems, and apparatuses for adaptive power supply voltage transient protection are disclosed. One system includes a power supply, a voltage transient sensor, and a power control processing entity. The power supply operates to provide power to one or more processors. The voltage transient sensor is connected to the power supply and operates to sense voltage transients on the power supply at greater than a predetermined speed or rate. The power control processing entity operates to receive a representation of the sensed voltage transients and adjust a power load based on the sensed voltage transients.

Abstract: Systems and methods are disclosed for predicting one or more medical conditions utilizing digital images and employing artificial intelligent algorithms. The system offers accurate predictions utilizing quantized pre-trained deep learning model. The pre-trained deep learning model is trained on data samples and later refined as the system processes more digital images or new medical conditions are incorporated. One pre-trained deep learning model is used to predict the probability of one or more medical conditions and identify locations in the digital image effected by the one or more medical conditions. Further, one pre-trained deep learning model utilizing additional data and plurality of digital images, forecasts rate of infection and spread of the medical condition over time.

Abstract: The present disclosure relates to a system and method of performing quantization of a neural network having multiple layers. The method comprises receiving a floating-point dataset as input dataset and determining a first shift constant for first layer of the neural network based on the input dataset. The method also comprises performing quantization for the first layer using the determined shift constant of the first layer. The method further comprises determining a next shift constant for next layer of the neural network based on output of a layer previous to the next layer, and performing quantization for the next layer using the determined next shift constant. The method further comprises iterating the steps of determining shift constant and performing quantization for all layers of the neural network to generate fixed point dataset as output.

Abstract: Methods, systems, and apparatuses for adaptive power supply voltage transient protection are disclosed. One system includes a system on a chip (SOC), wherein the SOC includes a power supply, a voltage transient sensor, and a power control processing entity. The power supply operates to provide power to one or more processors operating on the SOC. The voltage transient sensor is connected to the power supply and operates to sense voltage transients on the power supply at greater than a predetermined speed or rate. The power control processing entity operates to receive a digital representation of the sensed voltage transients and adjust a power load of the SOC based on the sensed voltage transients.

Abstract: Methods, systems and apparatuses for a custom artificial neural network (ANN) architecture are disclosed.

Abstract: Systems and methods are disclosed for predicting one or more medical conditions utilizing digital images and employing artificial intelligent algorithms. The system offers accurate predictions utilizing quantized pre-trained deep learning model. The pre-trained deep learning model is trained on data samples and later refined as the system processes more digital images or new medical conditions are incorporated. One pre-trained deep learning model is used to predict the probability of one or more medical conditions and identify locations in the digital image effected by the one or more medical conditions. Further, one pre-trained deep learning model utilizing additional data and plurality of digital images, forecasts rate of infection and spread of the medical condition over time.

Abstract: Systems and methods are disclosed for predicting one or more medical conditions utilizing digital images and employing artificial intelligent algorithms. The system offers accurate predictions utilizing quantized pre-trained deep learning model. The pre-trained deep learning model is trained on data samples and later refined as the system processes more digital images or new medical conditions are incorporated. One pre-trained deep learning model is used to predict the probability of one or more medical conditions and identify locations in the digital image effected by the one or more medical conditions. Further, one pre-trained deep learning model utilizing additional data and plurality of digital images, forecasts rate of infection and spread of the medical condition over time.

Abstract: Methods, systems and apparatuses for a custom artificial neural network (ANN) architecture are disclosed.

Abstract: Methods, systems, and apparatuses for unsupervised data drift detection for classification neural networks are disclosed.

Abstract: Methods, systems, and apparatuses for graph stream processing are disclosed. One apparatus includes a cascade of graph streaming processors, wherein each of the graph streaming processor includes a processor array, and a graph streaming processor scheduler. The cascade of graph streaming processors further includes a plurality of shared command buffers, wherein each shared command buffer includes a buffer address, a write pointer, and a read pointer, wherein for each of the plurality of shared command buffers a graph streaming processor writes commands to the shared command buffer as indicated by the write pointer of the shared command buffer and the graph streaming processor reads commands from the shared command buffer as indicated by the read pointer, wherein at least one graph streaming processor scheduler operates to manage the write pointer and the read pointer to avoid overwriting unused commands of the shared command buffer.

Abstract: Systems, apparatuses and methods are disclosed for efficient management of registers in a graph stream processing (GSP) system. The GSP system includes a thread scheduler module operative to initiate a Single Instruction Multiple Data (SIMD) thread, the SIMD thread including a dispatch mask with an initial value. A thread arbiter module operative to select an instruction from the instructions and provide the instruction to each of one or more compute resources, and an instruction iterator module, associated with the each of one or more compute resources operative to determine a data type of the instruction. The instruction iterator module iteratively executes the instruction based on the data type and the dispatch mask.