Real-Time Input Conditioning for Faster Sequence Model Inference
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Solution Overview
Problem
Existing machine-learned models for real-time applications face challenges due to high parameter counts, requiring significant memory, bandwidth, and processing power, making them inefficient and impractical for real-time inference on consumer devices.
Innovation Solution
Implementing a structured preprocessing and postprocessing framework that subdivides tasks into discrete operations, using lightweight models to preprocess and curate inputs for a primary machine-learned model, reducing the computational burden and enabling efficient, real-time processing on consumer devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high parameter count models are used for real-time processing, then model accuracy and capability are improved, but processing time and resource consumption increase significantly
Solution Approach 1:
The patent divides the model processing pipeline into distinct segments: a lightweight preprocessing model that prepares input data, and a primary sequence processing model that performs the main task. This segmentation allows the heavy model to operate on pre-processed, optimized inputs, reducing its processing time while maintaining accuracy.
Solution Approach 2:
The preprocessing model performs preparatory actions on input data before it reaches the main sequence processing model. By curating and optimizing inputs in advance, the system reduces the computational burden on the primary model, enabling faster real-time processing without sacrificing model capability.
2Reliability
If high parameter count models are deployed, then model performance is improved, but memory and bandwidth requirements increase
Solution Approach 1:
The system segments the computational workload between a small preprocessing model and a larger primary model. The preprocessing model requires minimal memory and bandwidth, while the primary model processes only the curated outputs, reducing overall resource demands compared to running the large model on all raw inputs.
Solution Approach 2:
The preprocessing model extracts and prepares only the most relevant features and inputs before passing them to the primary model. This extraction reduces the volume of data that needs to be transferred and processed, thereby reducing bandwidth and memory requirements while maintaining model performance.
3Adaptability or versatility
If complex model architectures are used, then processing capability is improved, but device complexity and implementation difficulty increase
Solution Approach 1:
The complex processing capability is segmented into a simple preprocessing model and a primary sequence processing model. This segmentation allows each component to be independently optimized and deployed, reducing the immediate complexity burden on the device while maintaining overall sophisticated processing capability.
4Speed
If lightweight models are used, then processing speed is improved, but model accuracy and capability decrease
Solution Approach 1:
The system merges the strengths of lightweight and complex models by combining a fast preprocessing model with a capable primary sequence processing model. The preprocessing model provides speed advantages, while the primary model ensures high accuracy, achieving both objectives simultaneously through their coordinated operation.
Data Source
AI summary
An example system provides real-time input conditioning for processing queries with machine-learned systems and models. Input conditioning can include processing an initial or raw user input and intelligently curating context data and instructions for input to a machine-learned model to perform a task associated with the user action. Input conditioning can significantly improve the performance of a machine-learned model compared to simply passing raw user inputs.