Abstract: A tensor traversal engine in a processor system comprising a source memory component and a destination memory component, the tensor traversal engine comprising: a control signal register storing a control signal for a strided data transfer operation from the source memory component to the destination memory component, the control signal comprising an initial source address, an initial destination address, a first source stride length in a first dimension, and a first source stride count in the first dimension; a source address register communicatively coupled to the control signal register; a destination address register communicatively coupled to the control signal register; a first source stride counter communicatively coupled to the control signal register; and control logic communicatively coupled to the control signal register, the source address register, and the first source stride counter.

Abstract: Application prototyping systems and methods are disclosed. One aspect is a processing method for multiple computing devices that includes identifying resource constraints for the multiple computing devices. Using identified resource constraints, a presentation model having a plurality of modifiable parameters based at least in part based on the resource constraints is created. At least one inference engine supporting neural network processing is used to execute a particular neural network model based at least in part on the presentation model.

Abstract: Application prototyping systems and methods are disclosed. One aspect is a processing method for multiple computing devices that includes identifying resource constraints for the multiple computing devices. Using identified resource constraints, multiple presentation models at least in part based on identified processing metrics are created. In one aspect, the multiple presentation models include multiple processing pipelines configurable for execution on multiple computing devices. An inference engine can be used to provide an execution model for the multiple processing pipelines based at least in part on the multiple presentation models, with the execution model having improved processing metrics as compared to at least one of the multiple presentation models.

Abstract: Proxy systems and methods for multiprocessing architectures are described. One method includes receiving an inference request and a statistics request from a client computing system. The method may access a load state of each processing device in a subset of processing devices preloaded with the neural network model, and select a target processing device from the subset based on the load states. One aspect includes transmitting the inference request to the target processing device, and monitoring an execution of the inference request by the target processing device based on the neural network model. The method may receive an inference result generated by the target processing device after executing the inference request, and compute an average inference time for the inference request execution based on the monitoring. The method may transmit the inference result and the average inference time to the client computing system.

Abstract: Proxy systems and methods for multiprocessing architectures are described. One method includes receiving a neural network model from a client computing system. System resource availability on a plurality of processing devices may be assessed, and a subset of available processing devices may be selected based on the system resource availability. In one aspect, the neural network model is loaded into each processing device in the subset. The method may include receiving an inference request from the client computing system. A load state of each processing device in the subset may be accessed, and a target processing device from the subset may be selected based on the load states. The inference request may be transmitted to the target processing device.

Abstract: A tensor traversal engine in a processor system comprising a source memory component and a destination memory component, the tensor traversal engine comprising: a control signal register storing a control signal for a strided data transfer operation from the source memory component to the destination memory component, the control signal comprising an initial source address, an initial destination address, a first source stride length in a first dimension, and a first source stride count in the first dimension; a source address register communicatively coupled to the control signal register; a destination address register communicatively coupled to the control signal register; a first source stride counter communicatively coupled to the control signal register; and control logic communicatively coupled to the control signal register, the source address register, and the first source stride counter.

Abstract: A tensor traversal engine in a processor system comprising a source memory component and a destination memory component, the tensor traversal engine comprising: a control signal register storing a control signal for a strided data transfer operation from the source memory component to the destination memory component, the control signal comprising an initial source address, an initial destination address, a first source stride length in a first dimension, and a first source stride count in the first dimension; a source address register communicatively coupled to the control signal register; a destination address register communicatively coupled to the control signal register; a first source stride counter communicatively coupled to the control signal register; and control logic communicatively coupled to the control signal register, the source address register, and the first source stride counter.

Abstract: A tensor traversal engine in a processor system comprising a source memory component and a destination memory component, the tensor traversal engine comprising: a control signal register storing a control signal for a strided data transfer operation from the source memory component to the destination memory component, the control signal comprising an initial source address, an initial destination address, a first source stride length in a first dimension, and a first source stride count in the first dimension; a source address register communicatively coupled to the control signal register; a destination address register communicatively coupled to the control signal register; a first source stride counter communicatively coupled to the control signal register; and control logic communicatively coupled to the control signal register, the source address register, and the first source stride counter.

Abstract: A method for scheduling an artificial neural network includes: accessing a processor representation of a multicore processor comprising processor cores, direct memory access cores, and a cost model; and accessing a network structure defining a set of layers. The method also includes, for each layer in the set of layers: generating a graph based on the processor representation, the graph defining compute nodes, data transfer nodes, and edges representing dependencies between the compute nodes and the data transfer nodes; and generating a schedule for the layer based on the graph, the schedule assigning the compute nodes to the processor cores and assigning the data transfer nodes to the direct memory access cores. The method further includes aggregating the schedule for each layer in the set of layers to generate a complete schedule for the artificial neural network.