Transformer Token Halting for Low-Latency Object Detection
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Solution Overview
Problem
Transformer-based machine learning models used in autonomous vehicles face challenges in balancing efficiency and accuracy, particularly in real-time safety-critical systems, where higher latency hinders performance due to increased computational complexity from attention mechanisms.
Innovation Solution
Implementing a halting module within the transformer-based machine learning models to dynamically prune or halt tokens, utilizing a token recycling mechanism, and employing non-uniform token sparsity loss to reduce computational complexity and improve learning, while maintaining accuracy through a deterministic approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If transformer-based machine learning models use attention mechanisms for object detection, then detection accuracy is improved, but computational complexity increases and latency increases
Solution Approach 1:
The patent segments the token processing by introducing a halting module that divides tokens into two groups: halted tokens (pruned early) and non-halted tokens (processed through full attention). This segmentation allows the system to apply full computational attention only to relevant tokens while pruning irrelevant ones, thereby reducing overall computational complexity while maintaining detection accuracy for critical objects.
Solution Approach 2:
The patent extracts and removes redundant or less important tokens from the processing pipeline through the halting module. By identifying and halting tokens that contribute minimally to detection accuracy, the system reduces computational complexity without significantly impacting the overall detection performance, effectively taking out unnecessary computational overhead.
2Measurement precision
If transformer-based machine learning models process all tokens through attention mechanisms, then detection accuracy is improved, but latency increases
Solution Approach 1:
The halting module performs preliminary action by evaluating and halting tokens before they enter the full attention mechanism processing pipeline. This preliminary filtering removes irrelevant tokens in advance, preventing them from consuming computational resources and time in subsequent processing stages, thereby reducing overall latency while preserving accuracy for important tokens.
Solution Approach 2:
The patent applies partial action by processing only a subset of tokens (non-halted tokens) through the full attention mechanism rather than all tokens. This selective processing approach reduces the time required for computation while maintaining sufficient detection accuracy by focusing computational effort on the most relevant tokens for object detection.
3Productivity
If the model prunes tokens to reduce computational complexity, then processing efficiency is improved, but detection accuracy may deteriorate
Solution Approach 1:
The halting module incorporates feedback mechanisms that evaluate token importance and adjust halting decisions accordingly. By using feedback from detection results and token relevance assessments, the system can refine which tokens to prune and which to retain, ensuring that pruning decisions do not significantly degrade detection accuracy while maintaining processing efficiency improvements.
Data Source
AI summary
Systems and techniques are provided for performing object detection using a machine learning model with a transformer architecture. An example method can include receiving a plurality of tokens corresponding to segmented sensor data; identifying, by a halting module within the machine learning model, at least one halted token from the plurality of tokens, wherein the at least one halted token is excluded from a plurality of non-halted tokens provided as input to a subsequent layer during inference of the machine learning model; and detecting, by the machine learning model, at least one detected object based at least on the plurality of non-halted tokens.