Hardware development workload estimation method and device, terminal and storage medium
A workload and hardware technology, applied in neural learning methods, prediction, biological neural network models, etc., can solve problems such as low efficiency and accuracy, low flexibility, and increased workload of experts
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Embodiment 1
[0041] This embodiment provides a method for estimating the workload of hardware development, which uses a neural network model to estimate the workload to improve the accuracy and flexibility of the estimation.
[0042] Such as figure 1 As shown, the method includes the following steps:
[0043] S1, obtaining the complexity factor related to the workload;
[0044] S2, compose the obtained complexity factor and the corresponding workload into a training sample, use the complexity factor as input and the workload as output to perform neural network model training, and obtain a workload estimation model;
[0045] S3. Estimate the workload according to the workload estimation model.
[0046] In this method, the complexity factor related to the workload is obtained first, and the neural network model is trained with the complexity factor as the input and the workload as the output, and the obtained neural network model can be used as a workload estimation model for estimating th...
Embodiment 2
[0074] On the basis of Embodiment 1, this embodiment provides a device for estimating the workload of hardware development, such as image 3 As shown, the device includes the following functional modules.
[0075] Complexity factor obtaining module 101: obtain the complexity factor related to workload;
[0076] Workload estimation model training module 102: Compose the complexity factor and the corresponding workload to form a training sample, use the complexity factor as input, and the workload as output to perform neural network model training to obtain a workload estimation model;
[0077] Workload estimation module 103: perform workload estimation according to the workload estimation model.
[0078] Wherein, the complexity factor acquisition module 101 includes the following functional units.
[0079] Historical data acquisition unit 101-1: acquire hardware development historical data;
[0080] Factor statistics unit 101-2: Statistics of each complexity factor and corre...
Embodiment 3
[0089] This embodiment provides a terminal, and the terminal includes a processor and a memory.
[0090] The memory is used to store instructions for the processor to execute. The memory can be realized by any type of volatile or non-volatile storage terminal or their combination, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), Magnetic Memory, Flash Memory, Magnetic or Optical Disk. When the execution instructions in the memory are executed by the processor, the terminal is enabled to execute some or all of the steps in the foregoing method embodiments.
[0091] The processor is the control center of the storage terminal, using various interfaces and lines to connect various parts of the entire electronic terminal, by running or executing software programs and / or modules stored in the memory, and calling data ...
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