Dynamic NLU Execution with Caching to Reduce Latency and Cost
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Solution Overview
Problem
Current natural language understanding (NLU) processing systems face challenges in optimizing latency and computing cost, as they often execute multiple process flows serially or in parallel, leading to increased latency or redundant processing, without effectively determining the most efficient execution strategy for deriving semantic interpretations from user inputs.
Innovation Solution
Implementing a dynamic technique where NLU process flows are executed at least partially in parallel, with execution delays and caching of entity recognition results to avoid redundant processing, and using a machine learning model to determine the optimal execution strategy based on constraints such as latency, cost, and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If multiple NLU process flows are executed in parallel, then processing speed is improved, but computing cost and resource consumption increase
Solution Approach 1:
The system dynamically adjusts the execution strategy of NLU process flows based on real-time conditions. A machine learning model determines whether to execute flows in parallel or serially, and decides when to cache intermediate results, optimizing the balance between processing speed and computing cost adaptively rather than using a fixed approach
Solution Approach 2:
The system performs preliminary actions by caching intermediate results from entity recognition and other processing steps. This allows subsequent NLU process flows to reuse previously computed results without redundant processing, reducing computing cost while maintaining processing speed
2Use of energy by moving object
If multiple NLU process flows are executed serially, then computing cost is reduced, but processing latency increases
Solution Approach 1:
The system dynamically switches between serial and parallel execution modes based on the specific NLU task and conditions. The machine learning model evaluates whether parallel execution would benefit from cached results or if serial execution is more efficient, optimizing the trade-off between latency and computing cost
Solution Approach 2:
The system maintains continuity of useful action by caching intermediate results that can be continuously reused across multiple NLU process flows. This eliminates redundant processing and maintains high efficiency whether executing flows serially or in parallel
3Measurement precision
If entity recognition is performed in each NLU process flow, then processing accuracy is improved, but redundant processing increases computing cost
Solution Approach 1:
The system creates copies of intermediate results from entity recognition and stores them in a cache. Subsequent NLU process flows can copy and reuse these results instead of performing redundant entity recognition, maintaining accuracy while significantly reducing computing cost
Solution Approach 2:
The system discards redundant entity recognition operations and recovers previously computed results from the cache. This eliminates wasted computing resources while preserving the accuracy benefits of entity recognition through result reuse
4Device complexity
If NLU process flows are executed without caching, then processing simplicity is maintained, but processing latency increases due to redundant operations
Solution Approach 1:
The system performs preliminary entity recognition and caches the results before executing multiple NLU process flows. This preliminary action eliminates redundant processing in subsequent flows, reducing latency without significantly increasing system complexity
Solution Approach 2:
The system introduces a cache as an intermediary between entity recognition and NLU process flows. This intermediary stores intermediate results that can be efficiently retrieved and reused, reducing latency while maintaining relatively simple processing architecture
Data Source
AI summary
Devices and techniques are generally described for delayed execution of natural language understanding processes. In various examples, input data is received. In some examples, automatic speech recognition (ASR) data is generated that represents the input data. In some further examples, processing of the ASR data by a first natural language understanding (NLU) process is initiated. In some examples, a first amount of time by which to delay processing of the ASR data by a second NLU process is determined. In at least some examples, processing of the ASR data by the second NLU process is initiated after the first amount of time has elapsed. The first NLU process may be unable to interpret the ASR data. The second NLU process may generate result data that may be stored in memory.