Abstract: Various technologies pertaining to allocating computing resources of a neuromorphic computing system are described herein. Subgraphs of a neural algorithm graph to be executed by the neuromorphic computing system are identified. The subgraphs are each executed by a group of neuron circuits serially. Output data generated by execution of the subgraphs are provided to the same or a second group of neuron circuits at a same time or with associated timing data indicative of a time at which the output data was generated. The same or second group of neuron circuits performs one or more processing operations based upon the output data.

Abstract: A method for processing data is provided. Data is identified by a computer system. The data is processed in parallel by the computer system using temporal transformations to form pieces of temporal data. The pieces of temporal data are placed by the computer system in an order as the pieces of temporal data are generated by the temporal transformations to form a sequence of temporal data. The order of the sequence is based on a priority of when the pieces of temporal data should be processed, enabling performing an action.

Abstract: A data stream processing unit (DPU) and methods for its use and programming are disclosed. A DPU includes a number of processing elements (PEs) arranged in a physical sequence. Each datum in the data stream visits each PE in sequence. Each PE has a memory circuit, data and metadata input and output channels, and a computing circuit. The metadata input represents a partial computational state that is associated with each datum as it passes through the DPU. Each computing circuit implements a finite state machine that operates on the data and metadata inputs as a function of its position in the sequence and a data context, producing an altered partial computational state that accompanies the datum. When the data context changes, the current state of the finite state machine is stored, and a new state is loaded. The processing elements may be collectively programmed to perform any desired computation.

Abstract: A data stream processing unit (DPU) and method for use are provided. A DPU includes a number of processing elements arranged in a sequence, and each datum in the data stream visits each processing element in sequence. Each processing element has a memory circuit, data and metadata input and output channels, and a computing circuit. The metadata input represents a partial computational state that is associated with each datum as it passes through the DPU. The computing circuit for each processing element operates on the data and metadata inputs as a function of its position in the sequence, producing an altered partial computational state that accompanies the datum. Each computing circuit may be modeled, for example, as a finite state machine, and the collection of processing elements cooperate to perform the computation. The computing circuits may be collectively programmed to perform any desired computation.