Speculative Dataflow Node Execution for Latency Reduction
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Solution Overview
Problem
Conversational systems that rely on predefined templates struggle to handle complex or novel user utterances, limiting their ability to perform advanced actions and providing efficient responses.
Innovation Solution
Implementing a data-flow programming approach with a previously-trained code generation machine that generates specific data-flow programs for user utterances, allowing for efficient processing and execution of user requests, including speculative execution of program nodes based on partial utterances to reduce latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a data-flow programming approach with speculative execution is implemented, then processing efficiency and responsiveness are improved, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by predicting and executing program nodes before the complete user utterance is received. The code generation machine predicts the full data-flow program from partial utterance prefixes, and the speculative execution engine executes predicted nodes in advance, reducing latency and improving responsiveness.
Solution Approach 2:
The execution process is segmented into distinct phases: code generation from utterance prefixes, prediction of complete data-flow programs, selection of program nodes for speculative execution, and conditional execution based on complete utterance reception. This segmentation allows for manageable complexity in handling the speculative execution workflow.
2Loss of time
If speculative execution of program nodes is performed, then response latency is reduced, but computational resources are consumed
Solution Approach 1:
The system performs partial actions by executing only the predicted program nodes that are likely to be needed based on the utterance prefix. Not all nodes in the predicted data-flow program are executed; only those selected for speculative execution are run, conserving computational resources while still reducing latency for common operations.
Solution Approach 2:
The system uses feedback from the complete user utterance to validate the predicted program nodes. After receiving the complete utterance, the system compares the actual program needed with the predicted nodes and adjusts execution accordingly, ensuring that computational resources are not wasted on unnecessary operations.
Data Source
AI summary
A computer-implemented method is presented. The method comprises sequentially receiving a plurality of utterance prefixes, each sequentially received utterance prefix derived from a progressively longer incomplete portion of a full user utterance. For each sequentially received utterance prefix, a complete dataflow program is predicted based on the utterance prefix. The complete dataflow program includes a plurality of program nodes to be executed to satisfy the full user utterance. One or more program nodes are selected from the predicted complete dataflow program to speculatively execute based on at least the utterance prefix.