FP8 Source Arithmetic Instructions for Lower Memory Pressure
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine learning and artificial intelligence applications face challenges with memory and bandwidth issues due to the high computational intensity of deep neural network training, particularly when using classical IEEE-754 32-bit and 64-bit arithmetic, which can be alleviated by employing 8-bit floating-point operations.
Innovation Solution
Implementing 8-bit floating-point (FP8) arithmetic operations, including instructions for addition, subtraction, multiplication, division, and reciprocal calculations, with support for denormal inputs, various rounding modes, and optional up/down conversions to manage memory and bandwidth efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If classical IEEE-754 32-bit and 64-bit arithmetic is used, then numerical precision is maintained, but memory pressure and bandwidth requirements increase
Solution Approach 1:
The patent changes the precision parameter from classical 32-bit and 64-bit floating-point formats to 8-bit floating-point format. This parameter change reduces the memory footprint and bandwidth requirements by a factor of 4-8x while maintaining sufficient numerical precision for machine learning applications through careful design of the FP8 format and supporting operations
2Measurement precision
If classical IEEE-754 32-bit and 64-bit arithmetic is used, then numerical precision is maintained, but computational speed decreases
Solution Approach 1:
The patent changes the precision parameter from classical 32-bit and 64-bit floating-point formats to 8-bit floating-point format. This parameter change enables computational speedups of up to 2X by reducing the complexity of arithmetic operations while maintaining sufficient numerical precision for machine learning workloads
Solution Approach 2:
The patent segments the floating-point format into smaller 8-bit elements that can be processed more efficiently. By dividing the data into smaller units, the system can perform multiple operations in parallel and reduce the computational complexity of each individual operation, achieving higher throughput
3Quantity of substance
If 8-bit floating-point operations are implemented, then memory pressure and bandwidth requirements are reduced, but computational complexity increases
Solution Approach 1:
The patent merges multiple operations into fused-multiply-add (FMA) units that can perform multiplication and addition in a single operation cycle. This merging of operations reduces the overall computational complexity by eliminating intermediate storage and multiple operation sequences, while the 8-bit format keeps the individual operation complexity low
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Techniques for performing arithmetic operations on FP8 values are described. An exemplary instruction includes fields for an opcode, an identification of a location of a first packed data source operand, an identification of a location of a second packed data source operand, and an identification of location of a packed data destination operand, wherein the opcode is to indicate an arithmetic operation execution circuitry is to perform, for each data element position of the identified packed data source operands, the arithmetic operation on FP8 data elements in that data element position in FP8 format and store a result of each arithmetic operation into a corresponding data element position of the identified packed data destination operand.