Inference Tie-Breaker Layer for Reproducible Classification
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Solution Overview
Problem
Deep learning inference models face compatibility issues due to floating-point rounding errors, leading to inconsistent inference choices across different hardware-software stacks, especially when probabilities of multiple classes are close, causing categorical differences.
Innovation Solution
Implement a tie-breaker layer that deterministically selects an inference class by identifying a maximum probability and applying a threshold to handle near-ties, ensuring reproducible results across varying computing systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If floating-point arithmetic is used for deep learning inference, then computational flexibility and precision are improved, but rounding error accumulation leads to inconsistent inference choices across different hardware-software stacks
Solution Approach 1:
The patent introduces a tie-breaker layer as an intermediary component between the softmax layer and the output. This tie-breaker layer receives probability distributions from the softmax layer and applies deterministic tie-breaking rules to select final inference classes. The intermediary layer isolates the system from floating-point rounding errors by providing a standardized decision-making mechanism that produces consistent results across different hardware-software stacks, thereby resolving the contradiction between maintaining inference precision and ensuring inference consistency.
2Reliability
If a deterministic tie-breaker layer is added to ensure consistent inference outcomes, then inference consistency across different systems is improved, but system complexity increases
Solution Approach 1:
The patent segments the inference system into distinct functional layers: the softmax layer for probability computation and the tie-breaker layer for deterministic class selection. By dividing the system into these separate modules, each with a specific function, the patent achieves inference consistency without creating a monolithic complex system. The tie-breaker layer is a simple, standalone component that can be independently implemented and maintained, thus managing system complexity while ensuring reliable consistent outcomes across different hardware-software stacks.
Data Source
AI summary
Apparatuses, systems, and techniques to deterministically classify data. In at least one embodiment, inference classes with weights within a threshold range are treated as equivalent and one representative inference class is selected.