ML Inference Pre-Fetching for Natural Response Time
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Solution Overview
Problem
Machine learning systems often fail to provide responses within the required natural response time (NRT) or system-required response time (SRRT), leading to reduced realism and potential safety issues in interactions, especially in applications like de-escalation training and high-speed position correction systems.
Innovation Solution
A method involving pre-fetching and partial fetching, where possible inputs and their corresponding inferences are generated and stored, allowing for the selection and recall of pre-computed responses instead of real-time inference on actual inputs, using a bifurcated process with partial or full ML models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If real-time machine learning inference is performed on actual inputs, then the system can provide accurate responses, but the response time exceeds the natural response time (NRT) and system-required response time (SRRT)
Solution Approach 1:
The system pre-generates inferences for a comprehensive set of possible inputs before they are actually needed. These pre-computed inferences are stored and can be quickly retrieved when an actual input arrives, eliminating the need for real-time inference computation and thus meeting strict response time requirements without sacrificing accuracy.
Solution Approach 2:
The inference process is divided into two distinct phases: an offline pre-computation phase where inferences for possible inputs are generated and stored, and an online retrieval phase where pre-computed results are quickly accessed. This segmentation allows computationally intensive work to be done in advance, separating it from time-critical operations.
2Measurement precision
If the machine learning model complexity is increased to improve inference quality, then the accuracy and realism of interactions improve, but the computation time increases and latency rises
Solution Approach 1:
Complex inferences are performed in advance during an offline phase when computation time is not constrained. The results are cached and stored for rapid retrieval during online operations. This allows the use of sophisticated, high-accuracy models without incurring their computational cost during time-critical inference operations.
Solution Approach 2:
Instead of executing the full complex inference process in real-time, the system creates and stores copies of inference results for various possible inputs. When an actual input arrives, the system retrieves the pre-computed copy rather than regenerating it, thus maintaining high accuracy while dramatically reducing computation time.
3Loss of time
If pre-fetching of possible inputs and inferences is implemented, then latency is reduced and NRT/SRRT are met, but the device complexity and memory requirements increase
Solution Approach 1:
The system performs preliminary generation of possible inputs and their corresponding inferences, storing them in a structured format that enables efficient retrieval. This pre-computation approach trades memory resources for time resources, which is acceptable when response time is the critical constraint.
Solution Approach 2:
The pre-computed inference database serves multiple functions: it provides rapid response retrieval, handles various types of inputs through the comprehensive set of possible inputs, and can be updated or regenerated as needed. This multi-functionality justifies the added system complexity by providing versatile, high-performance responses across different scenarios.
Data Source
AI summary
Methods for providing a machine learning (ML) final inference to a user, wherein an ML model and a computer-based content generation system (CGS) receives possible inputs and generates possible inferences, which are stored in association with the possible inputs to a memory so that they may be recalled based on the possible inputs. After receiving an actual input and an acceptability criterion, the CGS identifies a possible input that acceptably matches the actual input by satisfying the acceptability criterion. If a match is identified, the CGS substitutes the matching possible input in place of the actual input and outputs the possible inference corresponding to the matching possible input as the final inference to a user or to a second ML model. When a match is identified, inference is never performed on the actual input and the possible inferences are generated prior to receipt of the actual input.