A system receives and executes a sequence of tensor instructions, for example, instructions for performing a neural network computation. The system may be implemented as a multiprocessor architecture, for example, hardware for performing a neural network computation. A tensor instruction specifies a tensor computation receiving one or more input tensors for determining an output tensor. The system stores a decimal position associated with a plurality of values of a tensor. The system performs the tensor computation of a tensor instruction to determine a plurality of values of the output tensor. The system collects statistics describing the plurality of values of the output tensor and determines a decimal position for the plurality of values based on the collected statistics.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer system comprising: one or more processors for performing a neural network computation; and a non-transitory computer readable storage medium storing instructions for executing a neural network computation by the one or more processors, the neural network computation specified as a sequence of tensor instructions, the instructions when executed by the one or more processors cause the one or more processors to: identify a tensor instruction from the sequence of tensor instructions, the tensor instruction specifying a tensor computation associated with the neural network computation, the tensor computation receiving one or more input tensors and determining an output tensor representing a result of the tensor computation; receive one or more input tensors for the identified tensor instruction, each input tensor comprising a plurality of values, each value stored as a number in a memory associated with the one or more processors; for each of the one or more input tensors, receive a decimal position associated with the plurality of values of the input tensor; determine a value of the decimal position for the plurality of values of the output tensor; determine a plurality of values of the output tensor by performing the tensor computation specified in the identified tensor instruction; and collect statistics describing an aggregate measure of sizes of values of the plurality of values of the output tensor for determining a subsequent value of the decimal position for the plurality of values of the output tensor. 2. The computer system of claim 1, wherein determining the subsequent value of the decimal position for the plurality of values of the output tensor causes the one or more processors to: receive a value of the decimal position for the plurality of values of the output tensor, the value determined prior to performing the tensor computation; determine whether to adjust the received value of the decimal position for the output tensor based on the collected statistics; responsive to determining to adjust, adjusting the received value of the decimal position; and providing the adjusted value of the decimal position as the determined value of the decimal position for the plurality of values of the output tensor. 3. The computer system of claim 2, wherein determining whether to adjust a value of the decimal position causes the one or more processors to: detect, an occurrence of one of: an underflow, an underutilization, or an overflow while determining the plurality of values of the output tensor; and responsive to detecting the occurrence of the underflow, the underutilization, or the overflow, adjust the decimal position for the plurality of values of the output tensor by a predetermined value. 4. The computer system of claim 3, wherein determining whether to adjust a value of the decimal position causes the one or more processors to: detect an occurrence of an overflow while determining the plurality of values of the output tensor; and responsive to detecting the occurrence of the overflow, move the decimal position for the plurality of values of the output tensor to the right by a predetermined value. 5. The computer system of claim 3, wherein determining whether to adjust a value of the decimal position causes the one or more processors to: detect an occurrence of an underflow while determining the plurality of values of the output tensor; and responsive to detecting the occurrence of the underflow, move the decimal position for the plurality of values of the output tensor to the left by a predetermined value. 6. The computer system of claim 3, wherein determining whether to adjust a value of the decimal position causes the one or more processors to: detect an occurrence of an underutilization while determining the plurality of values of the output tensor, wherein detecting the occurrence of underutilization of a value comprises checking if the value has more than a threshold number of leading zeroes; and responsive to detecting the occurrence of the underutilization, move the decimal position for the plurality of values of the output tensor to the left by a predetermined value. 7. The computer system of claim 1, wherein the collected statistics represents a maximum of the absolute values of the plurality of values. 8. The computer system of claim 1, wherein collecting statistics comprises: storing in a data structure, a plurality of metric values determined over a plurality of iterations of executions of the sequence of tensor instructions, each metric value indicating a measure of the size of values of the tensor in an iteration, each iteration representing an execution of the sequence of instructions; determining a metric representing variance of the plurality of metric values; and wherein the decimal position for the output tensor is determined based on the variance of the plurality of metric values. 9. A non-transitory computer readable storage medium storing instruction for executing a sequence of tensor instructions, the instructions for: identifying, by one or more processors, a tensor instruction from the sequence of tensor instructions, the tensor instruction specifying a tensor computation associated with the neural network computation, the tensor computation receiving one or more input tensors and determining an output tensor representing a result of the tensor computation; receiving, by the one or more processors, one or more input tensors for the identified tensor instruction, each input tensor comprising a plurality of values, each value stored as a number in a memory associated with the one or more processors; for each of the one or more input tensors, receiving a decimal position associated with the plurality of values of the input tensor; determining a value of the decimal position for the plurality of values of the output tensor; determining a plurality of values of the output tensor by performing the tensor computation specified in the identified tensor instruction; collecting statistics describing an aggregate measure of sizes of values of the plurality of values of the output tensor for determining a subsequent value of the decimal position for the plurality of values of the output tensor. 10. The non-transitory computer readable storage medium of claim 9, wherein the sequence of instructions performing tensor computations are executed repeatedly for performing a neural network computation. 11. The non-transitory computer readable storage medium of claim 9, wherein determining the subsequent value of the decimal position for the plurality of values of the output tensor comprises: receiving a value of the decimal position for the plurality of values of the output tensor, the value determined prior to performing the tensor computation; determining whether to adjust the received value of the decimal position for the output tensor based on the collected statistics; responsive to determining to adjust, adjusting the received value of the decimal position; and providing the adjusted value of the decimal position as the determined value of the decimal position for the plurality of values of the output tensor. 12. The non-transitory computer readable storage medium of claim 11, wherein determining whether to adjust a value of the decimal position comprises, repeating one or more times: detecting, an occurrence of one of: an underflow, an underutilization, or an overflow while determining the plurality of values of the output tensor; and responsive to detecting the occurrence of the underflow, the underutilization, or the overflow, adjusting the decimal position for the plurality of values of the output tensor by a predetermined value. 13. The non-transitory computer readable storage medium of claim 11, wherein determining whether to adjust a value of the decimal position comprises, repeating one or more times: detecting an occurrence of an overflow while determining the plurality of values of the output tensor; and responsive to detecting the occurrence of the overflow, moving the decimal position for the plurality of values of the output tensor to the right by a predetermined value. 14. The non-transitory computer readable storage medium of claim 11, wherein determining whether to adjust a value of the decimal position comprises, repeating one or more times: detecting an occurrence of an underflow while determining the plurality of values of the output tensor; and responsive to detecting the occurrence of the underflow, moving the decimal position for the plurality of values of the output tensor to the left by a predetermined value. 15. The non-transitory computer readable storage medium of claim 11, wherein determining whether to adjust a value of the decimal position comprises, repeating one or more times: detecting an occurrence of an underutilization while determining the plurality of values of the output tensor, wherein detecting the occurrence of underutilization of a value comprises checking if the value has more than a threshold number of leading zeroes; and responsive to detecting the occurrence of the underutilization, move the decimal position for the plurality of values of the output tensor to the left by a predetermined value. 16. The non-transitory computer readable storage medium of claim 9, wherein determining the value of the decimal position for the plurality of values of the output tensor comprises determining whether one or more of the plurality of values of the output tensor for the instruction are likely to perform one of: exceeding a threshold value responsive to a subsequent execution of the instruction, or reducing below a threshold value responsive to subsequent execution of the instruction. 17. The non-transitory computer readable storage medium of claim 9, wherein the plurality of values of the output tensor is a first plurality of values and the output tensor further comprises a second plurality of values, further storing instructions for: collecting statistics describing an aggregate measure of sizes of values of the second plurality of values of the output tensor; and determining a second value representing the decimal position for the second plurality of values of the output tensor based on the aggregate measure of sizes of values of the second plurality of values. 18. The non-transitory computer readable storage medium of claim 9, wherein the collected statistics represents a maximum of the absolute values of the plurality of values. 19. The non-transitory computer readable storage medium of claim 9, wherein performing the computation associated with the instruction is performed by a plurality of processing units, each processing unit associated with a portion of the tensor, wherein collecting statistics comprises: determining, by each of the plurality of processing units, a metric representing sizes of the values of the portion of the tensor associated with the processing unit; and sending, by each of the plurality of processing units, the metric to a host device. 20. The non-transitory computer readable storage medium of claim 19, wherein collecting statistics further comprises: receiving, by the host device, from each of the plurality of processing units, the metric representing sizes of the values of the portion of the tensor associated with the processing unit; determining, by the host device, a metric value describing the size of the values of the tensor by aggregating the metric values received from the plurality of processing units; determining, by the host device, a value representing the decimal position for the tensor based on the metric value describing the size of the values of the tensor; and sending, by the host device, to each of the plurality of processing units, the value representing the decimal position for the tensor. 21. The non-transitory computer readable storage medium of claim 19, further comprising: adding the metric value describing the size of the values of the tensor to a data structure representing a plurality of metric values, each metric value associated with a previous execution of the instruction. 22. The non-transitory computer readable storage medium of claim 19, wherein collecting statistics comprises: storing in a data structure, a plurality of metric values determined over a plurality of iterations of executions of the sequence of tensor instructions, each metric value indicating a measure of the size of values of the tensor in an iteration, each iteration representing an execution of the sequence of instructions; determining a metric representing variance of the plurality of metric values; and wherein the decimal position for the output tensor is determined based on the variance of the plurality of metric values. 23. A method for executing a sequence of instructions performing tensor computations, the method comprising: identifying, by one or more processors, a tensor instruction from the sequence of tensor instructions, the tensor instruction specifying a tensor computation associated with the neural network computation, the tensor computation receiving one or more input tensors and determining an output tensor representing a result of the tensor computation; receiving, by the one or more processors, one or more input tensors for the identified tensor instruction, each input tensor comprising a plurality of values, each value stored as a number in a memory associated with the one or more processors; for each of the one or more input tensors, receiving a decimal position associated with the plurality of values of the tensor; determining a value of the decimal position for the plurality of values of the output tensor; determining a plurality of values of the output tensor by performing the tensor computation specified in the instruction; collecting statistics describing an aggregate measure of sizes of values of the plurality of values of the output tensor for determining a subsequent value of the decimal position for the plurality of values of the output tensor. 24. The method of claim 23, wherein determining the subsequent value of the decimal position for the plurality of values of the output tensor comprises: receiving a value of the decimal position for the plurality of values of the output tensor, the value determined prior to performing the tensor computation; determining whether to adjust the received value of the decimal position for the output tensor based on the collected statistics; responsive to determining to adjust, adjusting the received value of the decimal position; and providing the adjusted value of the decimal position as the determined value of the decimal position for the plurality of values of the output tensor. 25. The method of claim 24, wherein determining whether to adjust a value of the decimal position comprises, repeating one or more times: detecting, while determining the plurality of values of the output tensor, an occurrence of one of: an underflow, an underutilization, or an overflow; and responsive to detecting the occurrence of the underflow, the underutilization, or the overflow, adjusting the decimal position for the plurality of values of the output tensor by a predetermined value. 26. The method of claim 23, wherein collecting statistics comprises: storing a plurality of metric values, each metric value indicating a measure of the size of values of the tensor in an iteration, each iteration representing an execution of the sequence of instructions; determining a metric representing variance of the plurality of metric values; and wherein determining whether to adjust a value of the decimal position for the output tensor is further based on the variance of the plurality of metric values.