A first input is processed via a first configuration of a neural network to produce a first output. The first configuration defines attributes of the neural network, such as connections between neural elements of the neural network. If the neural network requires a context switch to process a second input, a second configuration is applied to the neural network to change the attributes, and the second input is processed via the second configuration of the neural network to produce a second output.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: processing a first input using a first configuration of a neural network to produce a first output, the first configuration defining attributes of the neural network, the attributes including at least connections between neural elements of the neural network; determining that the neural network requires a context switch to process a second input; applying a second configuration to the neural network to change the attributes; and processing the second input using the second configuration of the neural network to produce a second output. 2. The method of claim 1, wherein the first and second configurations correspond to layers of a virtual neural network, and the second input comprises the first output. 3. The method of claim 2, wherein the neural network comprises neural network circuitry, and the virtual neural network is too large to be represented in the neural network circuitry. 4. The method of claim 1, wherein the first and second configurations correspond to first and second neural networks that produce parallel outputs. 5. The method of claim 4, wherein the neural network comprises neural network circuitry, and the first and second neural networks are too large to be represented in the neural network circuitry together. 6. The method of claim 1, wherein applying the second configuration to the neural network comprises loading the second configuration from a memory and copying the second configuration to a configuration register of the neural network. 7. The method of claim 6, wherein a persistent memory provides context configuration data out of order relative to data access requests, the method further comprising: receiving scheduling information regarding the data access requests directed to the persistent memory; and generate an ordering of context configuration to ensure a desired delivery order of the context configuration data. 8. The method of claim 6, wherein the neural network comprises two or more configuration registers, and copying the second configuration to the configuration register comprises: copying the second configuration to a second of the two or more configuration registers, wherein the first configuration is stored in a first of the two or more configuration registers; and signaling to the neural network to use the second configuration register. 9. The method of claim 1, wherein the attributes further comprise at least one of weightings of the connections and transfer functions of the neural elements. 10. The method of claim 1, further comprising pausing the processing of the first input before processing the second input. 11. An apparatus comprising: a neural network that is configurable by applying data that changes attributes of the neural network, the attributes including at least connections between neural elements of the neural network; a controller configured to: determine that the neural network requires a context switch to process a second input, the neural network currently processing a first input via a first configuration of the attributes of the neural network; apply a second configuration to the neural network to change the attributes; and process the second input via the second configuration of the neural network to produce a second output. 12. The apparatus of claim 11, wherein the first and second configurations correspond to layers of a virtual neural network. 13. The apparatus of claim 12, wherein the neural network comprises neural network circuitry, and the virtual neural network is too large to be represented in the neural network circuitry. 14. The apparatus of claim 11, wherein the first and second configurations correspond to first and second neural networks. 15. The apparatus of claim 14, wherein the neural network comprises neural network circuitry, and the first and second neural networks are too large to be represented in the neural network circuitry together. 16. The apparatus of claim 11, wherein applying the second configuration to the neural network comprises loading the second configuration from a persistent memory and copying the second configuration to a configuration register of the neural network. 17. The apparatus of claim 16, wherein the persistent memory provides context configuration data out of order relative to data access requests, the controller further configured to: receive scheduling information regarding the data access requests; and generate an ordering of context configuration to ensure a desired delivery order. 18. The apparatus of claim 16, wherein the neural network comprises two or more configuration registers, and copying the second configuration to the configuration register comprises: copying the second configuration to a second of the two or more configuration registers, wherein the first configuration is stored in a first of the two or more configuration registers; and signaling to the neural network to use the second configuration register. 19. The apparatus of claim 11, wherein the attributes further comprise at least one of weightings of the connections and transfer functions of the neural elements. 20. A system comprising: a host processor; and at least one storage compute device comprising a neural network and a processing unit coupled to the host processor, the processing unit configured to: process a first input via a first configuration of the neural network to produce a first output, the first configuration defining attributes of the neural network, the attributes including at least connections between neural elements of the neural network; determine that the neural network requires a switch to process a second input; apply a second configuration to the neural network to change the attributes; and process the second input via the second configuration of the neural network to produce a second output.