Neural Network Input Detection for Invalid Inference Prevention
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current neural network processors lack the capability to detect and signal improper input data, leading to invalid inference results and degradation of user experience and resource utilization.
Innovation Solution
A system comprising hardware circuits and an improper input detection module that compares neural network outputs with reference outputs based on configurable detection criteria to identify and handle improper input data, including notifications and suspension of computations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If neural network processors process input data without detection capability, then processing speed and productivity are maintained, but reliability of inference results deteriorates when input data deviate from training data
Solution Approach 1:
The system performs preliminary detection of improper input data before feeding it to the neural network processor. By detecting whether input data deviates from training data distribution in advance (using statistical tests or distance metrics), the system prevents unreliable inference results from being generated, thus improving reliability without requiring the processor itself to become more complex
Solution Approach 2:
An intermediary detection module is introduced between the data input source and the neural network processor. This mediator component analyzes input data characteristics and filters out improper data, allowing the neural network processor to maintain its simplicity while the system overall achieves higher reliability through the intermediary's protective function
2Loss of energy
If neural network processors continue processing without input validation, then productivity is maintained, but loss of energy increases due to wasteful processing of improper input data
Solution Approach 1:
The system performs preliminary validation of input data before processing to identify improper data in advance. By detecting data improperness beforehand (using statistical thresholds or distribution comparisons), the system prevents energy-wasting computations on invalid inputs, thereby reducing energy loss without significantly impacting overall productivity
Solution Approach 2:
The system rapidly skips or filters out improper input data through efficient detection mechanisms before it reaches the processing stage. By quickly identifying and discarding invalid data using simple statistical checks, the system avoids the energy consumption of processing useless inputs while maintaining high throughput for valid data
3Loss of information
If no detection module is implemented, then device complexity remains low, but loss of information occurs regarding the validity of input data
Solution Approach 1:
An intermediary detection module is positioned between data input and processing to provide information about input validity. This mediator component analyzes data characteristics and generates validity indicators, ensuring that information about whether input data is proper is preserved and communicated to downstream systems, preventing information loss without requiring complex integrated solutions
Solution Approach 2:
The system implements feedback mechanisms where the detection module provides information about input data validity back to the processing system. By monitoring input characteristics and feeding this information back (through flags, scores, or status signals), the system maintains awareness of data quality without requiring the entire system to become overly complex
Data Source
AI summary
Systems and methods for performing improper input data detection are described. In one example, a system comprises: hardware circuits configured to receive input data and to perform computations of a neural network based on the input data to generate computation outputs; and an improper input detection circuit configured to: determine a relationship between the computation outputs of the hardware circuits and reference outputs; determine that the input data are improper based on the relationship; and perform an action based on determining that the input data are improper.