Abstract: Systems and techniques are described herein for generating an image. For instance, a process can include generating, by a style adapter for a pretrained machine learning model, a style embedding based on an input style image; generating, by a content adapter for the pretrained machine learning model, a content embedding based on an input content image; combining the style embedding and the content embedding to generate a combined embedding; and generating, using the pretrained machine learning model, an output image based on the combined embedding.

Abstract: A method for optimizing the compilation of a machine learning model to be executed on target edge devices is provided. Compute nodes of a plurality of compute nodes are allocated to a compiler optimization process for a compiler of said machine learning model. The machine learning model has a compute graph representation having nodes that are kernel operators necessary to execute the machine learning model and edges that connect said kernel operators to define precedence constraints. A round of optimization is scheduled for the process amongst the allocated compute nodes. At each allocated compute node a sequencing and scheduling solution is applied per round to obtain a performance metric for the machine learning model. From each compute node the performance metric is received and a solution that has the best performance metric is identified and implemented for execution of the machine learning model on the target edge devices.

Abstract: A processor-implemented method includes receiving a graph representing an artificial neural network (ANN). The graph includes multiple nodes connected by edges and each node represents an operation. Retention intervals are determined for the multiple node outputs based on rematerialization constraints and paging constraints. The retention intervals correspond to a time interval for retaining each node output in at least one local memory. A sequence of tasks for executing the multiple nodes of the graph representing the ANN is determined based on the retention intervals.

Abstract: A processor-implemented method for rematerialization for an artificial neural network (ANN) includes receiving a graph representing the ANN. The graph includes multiple nodes connected by edges and each node represents an operation. Retention intervals for the nodes are determined based on a precedence constraint for the nodes. The retention intervals correspond to a time interval for retaining each node output in a local memory. One of the nodes to recompute is determined based on the retention intervals.

Abstract: A processor-implemented method for compiler optimization using node symmetry includes receiving a representation of an artificial neural network (ANN) include multiple nodes coupled via multiple edges. One or more symmetric sets of nodes are determined based on one or more of a set of attributes for each node or a connectivity of the nodes via the edges. One or more of an order or a schedule for executing the nodes is generated based on the one or more symmetric sets of nodes.

Abstract: A processor-implemented method includes generating, by a scheduling model, a group of schedules from a computation graph associated with a task, each node on the computation graph being associated with an operation of an artificial neural network, each schedule of the group of schedules associating each node of the computation graph with a processor of a group of processors of a hardware device. The processor-implemented method also includes testing one or more schedules of the group of schedules on the hardware device or a model of the hardware device. The processor-implemented method further includes selecting a schedule of the one or more schedules based on testing the one or more schedules, the selected schedule satisfying a selection condition.

Abstract: A processor-implemented method includes sampling, according to a priority sampling policy, a set of node priorities from a computation graph. Each node priority of the set of node priorities may be associated with a respective node on the computation graph. Additionally, each node may represent an operation of a task performed by an artificial neural network. The method also includes converting, via a list scheduling function, the node priorities to a schedule that associates each node of the computation graph with a processor of a group of processors of a device associated with the artificial neural network, the schedule associated with a makespan. The method further includes performing the task in accordance with the schedule.

Abstract: A processor-implemented method for generating a schedule for executing operations of a compute graph includes receiving a graph including multiples nodes connected by edges. Each of the multiple nodes represents an operation to be executed. A set of sequences for executing the nodes is determined based on one or more precedence constraints. One or more sequences are selected from the set of sequences based on a memory constraint associated with a device for executing the nodes. A schedule for executing the nodes on the device is generated based on the selected one or more sequences.

Abstract: A processor-implemented method for generating a topological order using an artificial neural network (ANN) includes receiving a set of tasks to be performed. The tasks are represented in a graph including multiple nodes connected by edges. Each node corresponds to a task in the set of tasks. A scheduling priority is assigned to each node in the graph. A next node of potential next nodes is selected according to a probability of each of the potential next nodes based on the assigned scheduling priorities and a topology of the graph. A topological order of the tasks is generated by repeating the selection of the next node.

Abstract: A method performs XNOR-equivalent operations by adjusting column thresholds of a compute-in-memory array of an artificial neural network. The method includes adjusting an activation threshold generated for each column of the compute-in-memory array based on a function of a weight value and an activation value. The method also includes calculating a conversion bias current reference based on an input value from an input vector to the compute-in-memory array, the compute-in-memory array being programmed with a set of weights. The adjusted activation threshold and the conversion bias current reference are used as a threshold for determining the output values of the compute-in-memory array.

Abstract: An apparatus and method for transmitting an indicator of channel quality while minimizing the use of a broadcast channel is described. A metric of forward link geometry of observed transmission signals is determined. An indicator of channel quality value is determined as a function of the observed transmission signals. An access sequence is selected, randomly, from one group of a plurality of groups of access sequences, wherein each of the plurality of groups of access sequences correspond to different ranges of channel quality values.

Abstract: The disclosure relates to drone user equipment (UE) indications that may be conveyed to a wireless network. In particular, a UE that has flight capabilities (i.e., capabilities to operate as an unmanned aircraft system) and optional further capabilities to report a current height level may indicate such capabilities to the wireless network. As such, the wireless network may differentiate the drone UE from other UEs that only operate on the ground. Furthermore, the optional current height level may enable the wireless network to differentiate among drone UEs operating at different heights and/or from other UEs that are operating on the ground. The wireless network may further use the information indicating the flight capabilities either alone or in combination with the optional height information to configure power control parameters, manage interference, provide mobility management functions, generate neighbor lists, control beamforming, or implement a radio resource configuration or management procedure.

Abstract: A method performs XNOR-equivalent operations by adjusting column thresholds of a compute-in-memory array of an artificial neural network. The method includes adjusting an activation threshold generated for each column of the compute-in-memory array based on a function of a weight value and an activation value. The method also includes calculating a conversion bias current reference based on an input value from an input vector to the compute-in-memory array, the compute-in-memory array being programmed with a set of weights. The adjusted activation threshold and the conversion bias current reference are used as a threshold for determining the output values of the compute-in-memory array.

Abstract: An apparatus and method for transmitting an indicator of channel quality while minimizing the use of a broadcast channel is described. A metric of forward link geometry of observed transmission signals is determined. An indicator of channel quality value is determined as a function of the observed transmission signals. An access sequence is selected, randomly, from one group of a plurality of groups of access sequences, wherein each of the plurality of groups of access sequences correspond to different ranges of channel quality values.

Abstract: The disclosure relates to drone user equipment (UE) indications that may be conveyed to a wireless network. In particular, a UE that has flight capabilities (i.e., capabilities to operate as an unmanned aircraft system) and optional further capabilities to report a current height level may indicate such capabilities to the wireless network. As such, the wireless network may differentiate the drone UE from other UEs that only operate on the ground. Furthermore, the optional current height level may enable the wireless network to differentiate among drone UEs operating at different heights and/or from other UEs that are operating on the ground. The wireless network may further use the information indicating the flight capabilities either alone or in combination with the optional height information to configure power control parameters, manage interference, provide mobility management functions, generate neighbor lists, control beamforming, or implement a radio resource configuration or management procedure.

Abstract: An apparatus and method for transmitting an indicator of channel quality while minimizing the use of a broadcast channel is described. A metric of forward link geometry of observed transmission signals is determined. An indicator of channel quality value is determined as a function of the observed transmission signals. An access sequence is selected, randomly, from one group of a plurality of groups of access sequences, wherein each of the plurality of groups of access sequences correspond to different ranges of channel quality values.

Abstract: An apparatus and method for transmitting an indicator of channel quality while minimizing the use of a broadcast channel is described. A metric of forward link geometry of observed transmission signals is determined. An indicator of channel quality value is determined as a function of the observed transmission signals. An access sequence is selected, randomly, from one group of a plurality of groups of access sequences, wherein each of the plurality of groups of access sequences correspond to different ranges of channel quality values.

Abstract: The disclosure relates to drone user equipment (UE) indications that may be conveyed to a wireless network. In particular, a UE that has flight capabilities (i.e., capabilities to operate as an unmanned aircraft system) and optional further capabilities to report a current height level may indicate such capabilities to the wireless network. As such, the wireless network may differentiate the drone UE from other UEs that only operate on the ground. Furthermore, the optional current height level may enable the wireless network to differentiate among drone UEs operating at different heights and/or from other UEs that are operating on the ground. The wireless network may further use the information indicating the flight capabilities either alone or in combination with the optional height information to configure power control parameters, manage interference, provide mobility management functions, generate neighbor lists, control beamforming, or implement a radio resource configuration or management procedure.

Abstract: The disclosure relates to drone user equipment (UE) indications that may be conveyed to a wireless network. In particular, a UE that has flight capabilities (i.e., capabilities to operate as an unmanned aircraft system) and optional further capabilities to report a current height level may indicate such capabilities to the wireless network. As such, the wireless network may differentiate the drone UE from other UEs that only operate on the ground. Furthermore, the optional current height level may enable the wireless network to differentiate among drone UEs operating at different heights and/or from other UEs that are operating on the ground. The wireless network may further use the information indicating the flight capabilities either alone or in combination with the optional height information to configure power control parameters, manage interference, provide mobility management functions, generate neighbor lists, control beamforming, or implement a radio resource configuration or management procedure.

Abstract: The disclosure relates to drone user equipment (UE) indications that may be conveyed to a wireless network. In particular, a UE that has flight capabilities (i.e., capabilities to operate as an unmanned aircraft system) and optional further capabilities to report a current height level may indicate such capabilities to the wireless network. As such, the wireless network may differentiate the drone UE from other UEs that only operate on the ground. Furthermore, the optional current height level may enable the wireless network to differentiate among drone UEs operating at different heights and/or from other UEs that are operating on the ground. The wireless network may further use the information indicating the flight capabilities either alone or in combination with the optional height information to configure power control parameters, manage interference, provide mobility management functions, generate neighbor lists, control beamforming, or implement a radio resource configuration or management procedure.