QNN Vs SNPE Validator: Why Results Differ?

by Rajiv Sharma 43 views

Hey guys! Ever wondered why the results from qnn-platform-validator and snpe-platform-validator might differ? It's a common head-scratcher in the world of AI and Qualcomm Neural Processing SDK (SNPE). Let's dive into the nitty-gritty details and figure out what's going on. We'll break down the commands, results, and potential reasons for these discrepancies. Buckle up; it's gonna be an informative ride!

Understanding the Validators

First, let's get acquainted with our main players: qnn-platform-validator and snpe-platform-validator. These are essential tools for developers working with Qualcomm platforms, ensuring that their devices can support the neural networks they're trying to deploy. Think of them as the gatekeepers of compatibility, making sure everything plays nicely together.

QNN Platform Validator

The qnn-platform-validator is primarily focused on validating the Qualcomm Neural Network (QNN) backend. This tool checks whether your device supports the QNN framework, which is crucial for running neural networks efficiently on Qualcomm hardware. It is designed to verify the presence and functionality of the QNN libraries and drivers on the device. This validator is your go-to for confirming that the fundamental QNN components are correctly installed and operational. It examines various aspects, including hardware availability, library versions, and core versions, providing a comprehensive overview of QNN support on your device. This validator typically checks the GPU and DSP backends to ensure they are properly configured for QNN operations. The tests include hardware checks, library validation, version verification, and unit tests to confirm basic functionality. When this validator succeeds, it indicates that the device is fundamentally capable of running QNN-based applications, setting the stage for further development and deployment.

SNPE Platform Validator

The snpe-platform-validator, on the other hand, is part of the SNPE SDK and checks the compatibility of the SNPE runtime with different hardware backends like GPU, DSP, and AIP (AI Processor). It goes a step further by not just checking for presence but also testing the actual runtime performance. This validator is more concerned with the SNPE runtime environment and its ability to execute neural networks on the device. It evaluates the availability and performance of different runtime options, ensuring that the device can handle the computational demands of SNPE-based applications. The SNPE platform validator assesses various runtimes, including GPU, DSP, and AIP, and performs runtime checks and unit tests to verify their operational status. It checks the availability of necessary libraries, versions, and core components, much like the QNN validator but with a specific focus on SNPE compatibility. One critical function of this validator is to initialize the SNPE-QNN backend, and any failures during this process can lead to discrepancies in the results compared to the QNN validator. Successful validation with SNPE indicates that the device is ready to deploy and run SNPE-optimized neural networks efficiently.

Decoding the Commands and Results

Now, let's break down the specific commands and results you shared. This will help us pinpoint the areas where the discrepancies arise and understand why they occur.

QNN Platform Validator Command and Results

The command used for the QNN platform validator is:

./qnn-platform-validator --backend all --coreVersion --libVersion --testBackend --debug --targetPath ./

This command instructs the validator to check all available backends (--backend all), including GPU and DSP. It also requests the core and library versions (--coreVersion --libVersion), runs a backend test (--testBackend), enables debug mode (--debug), and specifies the target path for output files (--targetPath ./).

Results Breakdown

  • GPU Backend:

    • Hardware: Supported
    • Libraries: Found
    • Library Version: OpenCL 2.0 Adreno(TM) 690
    • Core Version: Adreno(TM) 690
    • Unit Test: Passed

    This indicates that the GPU backend is fully functional and supports QNN, with the necessary libraries and drivers in place.

  • DSP Backend:

    • Hardware: Supported
    • Libraries: Found
    • Library Version: Not Found
    • Core Version: Hexagon Architecture V68
    • Unit Test: Passed

    Here, the DSP backend is supported, but the library version is not found. Despite this, the unit test passed, suggesting that the basic functionality is working, but version information might be missing or inaccessible.

SNPE Platform Validator Command and Results

The command used for the SNPE platform validator is:

./snpe-platform-validator --runtime dsp --coreVersion --libVersion --testRuntime --targetPath ./ --debug

This command focuses on the DSP runtime (--runtime dsp), requesting core and library versions (--coreVersion --libVersion), running a runtime test (--testRuntime), specifying the target path (--targetPath ./), and enabling debug mode (--debug).

Results Breakdown

  • GPU Runtime:

    • Unit Test: Passed
    • Library Version: OpenCL 2.0
    • Core Version: Adreno(TM) 690

    The GPU runtime passed the unit test, and the library and core versions are correctly identified, similar to the QNN validator results.

  • DSP Runtime:

    • Unit Test: Failed
    • Library Version: Not Found
    • Core Version: Hexagon Architecture V68
    • Errors: Snpe-QNN backend initialization failed for deviceId 0

    This is where the discrepancy becomes apparent. The DSP runtime test failed, and the same