Low-Precision Floating-Point Datapath for Energy-Efficient Deep Learning Inference
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Solution Overview
Problem
Deep learning inference workloads in graphics processing units (GPUs) face challenges in energy efficiency due to the high energy consumption of data paths, particularly with the use of high-precision floating-point formats like FP8, which offer higher accuracy but at the cost of lower energy efficiency compared to lower precision formats like INT8.
Innovation Solution
The implementation of low-precision floating-point data paths using mechanisms such as multiplier units for FP8 operands, adders for integer results, and logic for scaling, which reduce energy consumption by utilizing integer accumulation and per-vector scaled quantization (VS-Quant) techniques, enabling energy-accuracy tradeoffs in deep learning inference calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If low-precision floating-point formats (FP8) are used in data paths, then measurement precision is improved, but use of energy worsens compared to integer formats
Solution Approach 1:
The data path is segmented into distinct functional units: multiplier units that operate on low-precision floating-point operands and produce integer results, separate adder units for integer accumulation, and dedicated scaling logic. This segmentation allows each component to be optimized for its specific operation, enabling energy-efficient integer arithmetic while preserving floating-point accuracy where needed.
Solution Approach 2:
An integer intermediate representation is introduced as a mediator between floating-point input operands and the final floating-point output. The multiplier units convert FP8 operands to integer format for efficient computation, perform accumulation in integer domain, then apply scaling factors to convert back to floating-point format. This intermediary representation enables the system to leverage both the energy efficiency of integer arithmetic and the precision benefits of floating-point formats.
2Use of energy by moving object
If integer accumulation is used in the data path, then use of energy is reduced, but measurement precision may be lost
Solution Approach 1:
The system dynamically changes the precision parameter of intermediate representations based on the computational stage. During accumulation operations, the precision is reduced to integer format to maximize energy efficiency. During final output stages, scaling logic restores the precision by applying appropriate scale factors to convert integer results back to floating-point format, ensuring accuracy is preserved when needed.
3Use of energy by moving object
If per-vector scaled quantization (VS-Quant) techniques are applied, then energy efficiency is improved, but device complexity increases
Solution Approach 1:
The scaling logic is designed as a universal component that serves multiple functions: it converts integer multiplication results back to floating-point format, applies per-vector scale factors for quantization, and handles different precision formats (FP8, FP4, etc.). This multi-functional design reduces overall system complexity by consolidating what could be separate specialized units into a single versatile scaling unit that handles all precision conversion and quantization operations.
Data Source
AI summary
Mechanisms to exploit the inherent resiliency of deep learning inference workloads to improve the energy efficiency of computer processors such as graphics processing units with these workloads. The mechanisms provide energy-accuracy tradeoffs in the computation of deep learning inference calculations via energy-efficient floating point data path micro-architectures with integer accumulation, and enhanced mechanisms for per-vector scaled quantization (VS-Quant) of floating-point arguments.