A multi-processor system for data processing may utilize a plurality of different types of neural network processors to perform, e.g., learning and pattern recognition. The system may also include a scheduler, which may select from the available units for executing the neural network computations, which units may include standard multi-processors, graphic processor units (GPUs), virtual machines, or neural network processing architectures with fixed or reconfigurable interconnects.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A cloud-based neural network system for performing pattern recognition tasks, the system comprising: a heterogeneous combination of neural network processors, wherein the heterogeneous combination of neural network processors includes at least two neural network processors selected from the group consisting of: a reconfigurable interconnect neural network processor; a fixed-architecture neural network processor; a graphic processor unit; a multi-processor unit; and a virtual machine; wherein each neural network processor includes a plurality of processing units. 2. The system as in claim 1, wherein a respective pattern recognition task is assigned to execute on one of the neural network processors. 3. The system as in claim 2, wherein assignment of pattern recognition tasks is balanced to minimize the cost of processing. 4. The system as in claim 1, further comprising: a user application programming interface (API); an engineering API; and an administration API. 5. The system as in claim 1, wherein a respective pattern recognition task is executed using a neural network comprising multiple layers of nodes. 6. The system as in claim 5, wherein a respective layer of the multiple layers of nodes is executed on a different neural network processor from at least one other respective layer of the multiple layers of nodes. 7. The system as in claim 6, wherein one or more results from a respective neural network processor are pipelined to a successive neural network processor. 8. The system as in claim 7, wherein a respective neural network processor synchronously executes its respective layer of the multiple layers of nodes. 9. The system as in claim 5, wherein a respective neural network processor includes a plurality of inner product units (IPUs); and wherein at least one node is executed on more than one IPU. 10. The system as in claim 5, wherein a respective neural network processor contains a plurality of IPUs; and wherein at least one IPU executes more than one node. 11. A neural network processor, comprising: a plurality of inner product units (IPUs), wherein a respective IPU performs at least one of: successive fixed-point multiply and add operations; successive floating-point multiply and add operations; successive sum operations; or successive compare operations; 12. The neural network processor as in claim 11, wherein a respective IPU is configured to output, after all input values to the neural network processor have been processed, a result selected from the group consisting of: a fixed-point result; a floating-point result; an average; a maximum; and a minimum. 13. The neural network processor as in claim 11, further comprising: an input bus; and an output bus, wherein at least one word is simultaneously placed each of the input bus and the output bus. 14. A method of testing a neural network using a neural network test case comprising input data, intermediate outputs for respective levels of the neural network, final outputs, and a multi-word checksum, the method comprising: condensing the input data, intermediate outputs and final outputs into an output checksum; and comparing the output checksum with the multi-word checksum. 15. The method as in claim 14, wherein the condensing is performed using an exclusive-or function. 16. The method as in claim 14, wherein the output checksum and the multi-word checksum comprise a same number of words, and wherein the comparing comprises comparing a respective output checksum word with a corresponding multi-word checksum word. 17. A hierarchical processing network, comprising: a plurality of neural network configurations in a hierarchical organization, wherein the neural network configurations are configured to perform successive levels of pattern recognition, wherein each successive level is a more specific pattern recognition than a previous level.