Cross-Architecture Operator Testing

Differential testing for GPU/NPU neural network operators

Overview

This project develops a comprehensive differential testing pipeline to compare neural network operator semantics across different hardware architectures, specifically CUDA (GPU) and Huawei CANN (NPU).

Motivation

As AI models are deployed across diverse hardware platforms, ensuring consistent behavior of neural network operators becomes critical. Different implementations can produce subtly different results, leading to:

  • Model accuracy degradation
  • Numerical instabilities
  • Difficult-to-debug production issues

Approach

Our differential testing framework:

  1. Operator Instrumentation: Captures inputs and outputs of neural network operators
  2. Cross-Platform Execution: Runs identical workloads on both CUDA and CANN
  3. Semantic Comparison: Analyzes differences in numerical outputs
  4. Root Cause Analysis: Identifies sources of discrepancies (precision, algorithm differences, etc.)

Key Features

  • Automated Testing: Systematic exploration of operator behaviors
  • Precision Analysis: Quantifies numerical differences
  • Performance Profiling: Compares execution times across platforms
  • Debugging Support: Provides detailed diagnostics for reliability issues

Technical Stack

  • Frameworks: CUDA, Huawei CANN
  • Languages: Python, C++
  • Testing Tools: Custom differential testing harness
  • Analysis: Statistical comparison methods

Applications

  • GPU/NPU reliability debugging
  • Cross-platform model validation
  • Hardware accelerator certification
  • Compiler optimization verification

Status

Ongoing research project (2026.02 - Present).