Data Type Conversion Instructions for Deep Learning Efficiency
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Solution Overview
Problem
Current processors lack native support for BF16 and FP16 data types, limiting their ability to execute deep learning tasks efficiently due to precision and range constraints, and require additional power and area to support these formats.
Innovation Solution
The implementation of instructions such as VCVTNEEBF162PS and VCVTNEOBF162PS, which convert BF16 and FP16 values to FP32, allowing for execution on processors without native BF16 and FP16 support by appending trailing zeros and using rounding modes to handle denormals, enabling the execution of previously unsupported data types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If processors implement native BF16 and FP16 support, then computational efficiency and precision for deep learning tasks is improved, but device complexity and area requirements increase
Solution Approach 1:
The patent uses FP32 as an intermediary format to bridge between BF16/FP16 source data and the processor's native operations. Conversion instructions transform BF16/FP16 values into FP32, allowing the processor to handle low-precision data through its existing FP32 execution units without requiring dedicated BF16/FP16 hardware, thus maintaining computational efficiency while avoiding increased device complexity
Solution Approach 2:
The conversion process creates a copy of the BF16/FP16 data in FP32 format, allowing the original low-precision data to be preserved while working with high-precision representations. This copying approach enables the processor to perform calculations using familiar FP32 operations without modifying the underlying hardware architecture
2Use of energy by moving object
If processors implement native BF16 and FP16 support, then power consumption is reduced for deep learning tasks, but manufacturing area requirements increase
Solution Approach 1:
The patent makes the existing FP32 execution units universal by enabling them to process converted BF16/FP16 data through software-based conversion instructions. This multi-functionality allows a single hardware implementation to handle multiple precision formats without requiring separate dedicated circuits for each format, reducing manufacturing area while maintaining energy efficiency
Solution Approach 2:
The conversion instructions change the precision parameter of the data from BF16/FP16 to FP32, allowing the same hardware to operate efficiently across different precision requirements. By dynamically adjusting the data representation rather than the hardware configuration, the system reduces area requirements while adapting to different computational needs
3Adaptability or versatility
If conversion instructions are implemented, then compatibility with processors without native BF16/FP16 support is improved, but execution time increases due to additional conversion steps
Solution Approach 1:
The conversion from BF16/FP16 to FP32 is performed as a preliminary action before the main computational operations. By converting the data format in advance, the processor can then execute the actual deep learning algorithms using its native FP32 operations without repeated format conversions, minimizing the impact on overall execution time while maximizing compatibility
Data Source
AI summary
Techniques for data type conversion via instruction are described. An exemplary instruction is to include fields for an opcode, an identification of a source operand, and an identification of destination operand, wherein the opcode is to indicate instruction processing circuitry is to convert odd 16-bit floating point values from the identified source operand into 32-bit floating point values and store the 32-bit floating point values in data element positions of the identified destination operand.