Neural Network Debugging via CRC Signature Comparison
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Debugging complex neural networks is challenging due to the difficulty in identifying the source of errors among thousands of processing nodes and millions of parameters, as incorrect results are hard to trace back to specific layers or operations.
Innovation Solution
Incorporating debugging circuits that generate and compare CRC signatures for input and output data at each processing engine, reducing the amount of data logged and minimizing performance impact, allowing for efficient identification of error causes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If debugging circuits are added to each processing engine to generate and compare CRC signatures, then error identification capability is improved, but device complexity increases
Solution Approach 1:
The patent introduces CRC circuits as intermediary components that generate compact error detection codes (signatures) for input and output data. These CRC circuits act as mediators between the processing engine and the debugging system, enabling error identification without requiring direct analysis of the entire complex data flow through thousands of processing nodes. The CRC signature serves as an intermediary representation that simplifies error tracking while maintaining debugging capability.
2Measurement precision
If full input and output data are logged for debugging, then measurement precision is improved, but loss of time and productivity deteriorate
Solution Approach 1:
The patent extracts only the essential debugging information (CRC signatures) from the complete input and output data streams. Instead of logging all the raw data passing through the neural network, the system extracts compact error detection codes that capture the critical information needed for debugging. This extraction approach maintains debugging accuracy while dramatically reducing the data volume that needs to be stored and analyzed, thereby preserving system performance.
Solution Approach 2:
The patent transforms the debugging approach by changing the parameter being monitored from the complete data values to their CRC hash representations. This parameter transformation converts large amounts of detailed data into compact signatures that retain error-detection capability while occupying minimal storage space and requiring minimal processing time for comparison and analysis.
3Reliability
If debugging circuits are added to each processing engine, then error detection capability is improved, but loss of energy increases
Solution Approach 1:
The patent employs lightweight CRC circuits that consume minimal energy compared to full debugging implementations. These compact error detection circuits generate small signatures that require little computational resources to produce and compare. The low energy cost of generating and comparing CRC signatures makes the debugging system sustainable for deployment across all processing engines in the neural network without significantly increasing overall energy consumption.
Data Source
AI summary
Techniques disclosed herein relate generally to debugging complex computing systems, such as those executing neural networks. A neural network processor includes a processing engine configured to execute instructions to implement multiple layers of a neural network. The neural network processor includes a debugging circuit configured to generate error detection codes for input data to the processing engine or error detection codes for output data generated by the processing engine. The neural network processor also includes an interface to a memory device, where the interface is configured to save the error detection codes generated by the debugging circuit into the memory device. The error detection codes generated by the debugging circuit are compared with expected error detection codes generated using a function model of the neural network to identify defects of the neural network.