Device and method for executing vector comparison operation

A technology for comparing operations and executing vectors, applied in machine execution devices, digital value comparisons, complex mathematical operations, etc., can solve problems such as inability to efficiently handle large-scale vector comparison operations, and achieve the effect of convenient use of instructions
CN107315563AActive Publication Date: 2017-11-03CAMBRICON TECH CO LTD
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
CAMBRICON TECH CO LTD
Publication Date
2017-11-03

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Abstract

The invention discloses a device and a method for executing vector comparison operation. The device comprises a storage device, a register unit, a control unit and a vector comparison unit, wherein the storage unit is used for storing vector data relevant to a vector comparison operation instruction; the register unit is used for storing scalar data relevant to the vector comparison operation instruction; the control unit is used for decoding the vector comparison operation instruction and controlling the operation process of the vector comparison operation instruction; and the vector comparison unit is used for performing vector comparison operation on two pieces of to-be-compared input vector data according to the decoded vector comparison operation instruction, wherein the vector comparison unit is a custom hardware circuit. Through the device and the method for executing vector comparison operation, a complete process of the simple vector comparison operation instruction is realized through the custom hardware circuit, that is, vector comparison operation can be realized through the simple vector comparison instruction.
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Description

technical field

[0001] The invention relates to the field of computer instruction operations, in particular to a device and method for performing vector comparison operations. Background technique

[0002] Vector comparison, that is, for two vectors with the same length, the corresponding elements are compared, and the obtained comparison results form a new output vector. In the field of deep learning, there is a need to perform size comparison operations on two vectors. In the restricted Boltzmann machine of the artificial neural network, there is such a step that requires sampling a vector composed of a group of neurons, that is, in the vector Each neuron is compared with a random number. If the value of the neuron is greater than the vector, it takes 1, otherwise it takes 0. Another example is to convert a group of 32-bit single-precision floating-point numbers into 16-bit half-precision floating-point numbers. If you choose the random carry method, you need to compare t...

Claims

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