Dual-ANN Interrupt Alerts for Low-Latency Stream Inference
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Solution Overview
Problem
Conventional artificial neural networks (ANNs) exhibit significant latency due to the need to fully process input data through multiple layers before generating inferences, which is problematic for systems requiring rapid responses.
Innovation Solution
Implementing a low latency ANN with fewer layers and nodes, allowing it to generate inferences before a primary ANN, and optionally preprocessing input data to further reduce latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional ANNs process data through multiple hidden layers to generate accurate inferences, then inference accuracy is improved, but processing latency increases significantly
Solution Approach 1:
The system segments the inference processing into two distinct parts: a low-latency ANN with fewer layers that provides quick preliminary inferences, and a primary ANN with more layers that generates accurate detailed inferences. This segmentation allows the system to balance speed and accuracy by handling different inference requirements separately.
Solution Approach 2:
The low-latency ANN performs a partial processing action by using fewer hidden layers to generate inferences faster, even though it may not achieve the same accuracy as the full primary ANN. This partial action is sufficient for time-critical applications where approximate results are acceptable.
2Adaptability or versatility
If ANNs include more hidden layers to perform sophisticated data analysis, then analysis capability is improved, but response time deteriorates
Solution Approach 1:
The system divides the analysis capability into two segments: the primary ANN with multiple hidden layers that provides sophisticated analysis capability, and the low-latency ANN with fewer layers that provides rapid response. Each segment is optimized for its specific function, allowing the system to achieve both high capability and fast response times.
Solution Approach 2:
The low-latency ANN serves as a simplified copy or approximation of the primary ANN, using fewer layers to replicate the essential analysis function. This copying approach enables fast responses while the full primary ANN handles complex analysis when time permits.
3Measurement precision
If the system waits for the primary ANN to generate detailed inferences before responding, then response accuracy is improved, but system responsiveness deteriorates
Solution Approach 1:
The low-latency ANN performs preliminary inference generation before the primary ANN completes its detailed analysis. This preliminary action provides immediate responses for time-critical events, while the primary ANN continues to generate more accurate inferences that can update or refine the initial response if needed.
Solution Approach 2:
The system implements feedback by allowing the primary ANN's detailed inferences to refine or correct the low-latency ANN's preliminary inferences. This feedback mechanism ensures that response accuracy can be improved over time while maintaining initial responsiveness.
Data Source
AI summary
Various techniques are provided for providing neural networks with increased efficiency. In one example, a system includes a first artificial neural network (ANN), a second ANN, and a logic device. The first ANN is configured to receive a first plurality of data inputs associated with a data stream and process the first data inputs to generate a first inference output after a first latency. The second ANN is configured to receive a second plurality of data inputs associated with the data stream and process the second data inputs to generate a second inference output after a second latency less than the first latency. The logic device is configured to receive the second inference output before the first inference output is generated. Additional systems and methods are also provided.