Embodiments of a processor are disclosed for performing arithmetic operations on a machine independent number format. The processor may include a floating point unit, and a number unit. The number format may include a sign/exponent block, a length block, and multiple mantissa digits. The number unit may be configured to perform an operation on two operands by converting the digit format of each mantissa digit of each operand, to perform the operation using the converted mantissa digits, and then to convert each mantissa digit of the result of the operation back into the original digit format.

Embodiments of a processor are disclosed for performing arithmetic operations on a machine independent number format. The processor may include a floating point unit, and a number unit. The number format may include a sign/exponent block, a length block, and multiple mantissa digits. The number unit may be configured to perform an operation on two operands by converting the digit format of each mantissa digit of each operand, to perform the operation using the converted mantissa digits, and then to convert each mantissa digit of the result of the operation back into the original digit format.

The present disclosure provides a data computing system. The data computing system comprises: a memory, a processor and an accelerator, wherein the memory is communicatively coupled to the processor and configured to store data to be computed and a computed result, the data being written by the processor; the processor is communicatively coupled to the accelerator and configured to control the accelerator; and the accelerator is communicatively coupled to the memory and configured to access the memory according to pre-configured control information, implement a computing process to produce the computed result and write the computed result back to the memory. The present disclosure also provides an accelerator and a method performed by an accelerator of a data computing system. The present disclosure can improve the execution efficiency of the processor and reduce the computing overhead of the processor.

The present disclosure provides a computation device. The computation device is configured to perform a machine learning computation, and includes an operation unit, a controller unit, and a conversion unit. The storage unit is configured to obtain input data and a computation instruction. The controller unit is configured to extract and parse the computation instruction from the storage unit to obtain one or more operation instructions, and to send the one or more operation instructions and the input data to the operation unit. The operation unit is configured to perform operations on the input data according to one or more operation instructions to obtain a computation result of the computation instruction. In the examples of the present disclosure, the input data involved in machine learning computations is represented by fixed-point data, thereby improving the processing speed and efficiency of training operations.

The present disclosure provides a computation device. The computation device is configured to perform a machine learning computation, and includes an operation unit, a controller unit, and a conversion unit. The storage unit is configured to obtain input data and a computation instruction. The controller unit is configured to extract and parse the computation instruction from the storage unit to obtain one or more operation instructions, and to send the one or more operation instructions and the input data to the operation unit. The operation unit is configured to perform operations on the input data according to one or more operation instructions to obtain a computation result of the computation instruction. In the examples of the present disclosure, the input data involved in machine learning computations is represented by fixed-point data, thereby improving the processing speed and efficiency of training operations.

The present disclosure provides a data computing system. The data computing system comprises: a memory, a processor and an accelerator, wherein the memory is communicatively coupled to the processor and configured to store data to be computed and a computed result, the data being written by the processor; the processor is communicatively coupled to the accelerator and configured to control the accelerator; and the accelerator is communicatively coupled to the memory and configured to access the memory according to pre-configured control information, implement a computing process to produce the computed result and write the computed result back to the memory. The present disclosure also provides an accelerator and a method performed by an accelerator of a data computing system. The present disclosure can improve the execution efficiency of the processor and reduce the computing overhead of the processor.

The present disclosure provides a computation device. The computation device is configured to perform a machine learning computation, and includes an operation unit, a controller unit, and a conversion unit. The storage unit is configured to obtain input data and a computation instruction. The controller unit is configured to extract and parse the computation instruction from the storage unit to obtain one or more operation instructions, and to send the one or more operation instructions and the input data to the operation unit. The operation unit is configured to perform operations on the input data according to one or more operation instructions to obtain a computation result of the computation instruction. In the examples of the present disclosure, the input data involved in machine learning computations is represented by fixed-point data, thereby improving the processing speed and efficiency of training operations.

The present disclosure provides a computation device. The computation device is configured to perform a machine learning computation, and includes an operation unit, a controller unit, and a conversion unit. The storage unit is configured to obtain input data and a computation instruction. The controller unit is configured to extract and parse the computation instruction from the storage unit to obtain one or more operation instructions, and to send the one or more operation instructions and the input data to the operation unit. The operation unit is configured to perform operations on the input data according to one or more operation instructions to obtain a computation result of the computation instruction. In the examples of the present disclosure, the input data involved in machine learning computations is represented by fixed-point data, thereby improving the processing speed and efficiency of training operations.

Principle and implementation of a new decimal electronic computer are provided in this disclosure, which belongs to field of electronic computers. A traditional computer was invented by Americans in 1946, which is binary, and data in the computer has only two states of 0 and 1. The binary states 0 and 1 are combined to represent various symbols and numbers, and various registers for binary algorithm are used to complete computation. Core of this disclosure is to use unit decimal data, a 10-bit hardware computation register group directly uses decimal numbers for computation, and one number has 10 states, so that operation and output of the decimal data can be directly completed. In the decimal computer, a CPU is composed of decimal computing register hardware at the bottom, which together with an auxiliary crossbar control circuit, a decimal memory and a decimal operating system, forms a complete decimal computer system.

Reduced logic conversion of binary integers to binary coded decimals, including: generating, from an input binary integer, an intermediate value comprising all zero digits encoded in an intermediate format; until each bit of the input binary integer has been shifted into the intermediate value: shifting a bit of the input binary integer into the intermediate value; doubling the intermediate value; converting the intermediate value to a binary encoded decimal output; and wherein the intermediate format comprises, for each digit of the intermediate value, a plurality of bits corresponding to a plurality of even weights, a first bit corresponding to a one weight, and a second bit corresponding to an inverse of the one weight.