Fixed-Point Conversion Unit for Faster Machine Learning Computation
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Solution Overview
Problem
General purpose processors are inefficient in processing large loads, leading to delays in machine learning tasks such as training artificial intelligence models, due to their limited operating speed.
Innovation Solution
A computation device and method that includes a controller unit, operation unit, and conversion unit, utilizing a primary processing circuit and secondary processing circuits to perform machine learning computations by converting input data into fixed-point data and executing operations efficiently, with a DMA unit and storage unit for data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If general purpose processors are used to process information, then the device can handle various tasks with flexibility, but the processing speed and efficiency deteriorate when handling large loads such as machine learning training operations
Solution Approach 1:
The processor is divided into a control unit and multiple operation units with different data types (first operation unit for first data type, second operation unit for second data type). This segmentation allows each unit to be optimized for specific operations while maintaining overall system versatility through the controller's ability to direct different data types to appropriate units.
Solution Approach 2:
The system changes the data type parameter by converting between first data type and second data type based on operation requirements. The controller unit determines when conversion is needed and directs data through appropriate conversion paths, enabling the system to optimize processing speed for different operational contexts while maintaining adaptability.
2Adaptability or versatility
If general purpose processors are used for machine learning training, then the system can perform the operations, but the computation time becomes extraordinarily long due to limited operating speed
Solution Approach 1:
The processor is divided into a control unit and multiple operation units with different data types (first operation unit for first data type, second operation unit for second data type). This segmentation allows each unit to be optimized for specific operations while maintaining overall system versatility through the controller's ability to direct different data types to appropriate units.
Solution Approach 2:
The system changes the data type parameter by converting between first data type and second data type based on operation requirements. The controller unit determines when conversion is needed and directs data through appropriate conversion paths, enabling the system to optimize processing speed for different operational contexts while maintaining adaptability.
Data Source
AI summary
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.